ARTICLE IN PRESS Engineering Applications of Artificial Intelligence i(l)Ill-lll Contents lists available at science Direct Artificial Engineering Applications of Artificial Intelligence ellgence ELSEVIER journalhomepagewww.elsevier.com/locate/engappai An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles x Yolanda blanco-Fernandez, Martin Lopez-Nores, Jose. Pazos-Arias, Jorge Garcia-Duque ETSE Telecomunicacion, Campus Universitario, vigo. 36310, Spain ARTICLE INFO ABSTRACT Recommender systems in online shopping automatically select the most appropriate items to each user, thus shortening his/her product searching time in the shops and adapting the selection as his/he particular preferences evolve over time. This adaptation process typically considers that a users 24 February 2011 Accepted 25 February 2011 nterest in a given type of product always decreases with time from the moment of the last purchase. However, the necessity of a product for a user depends on both the nature of the own item and the ersonal preferences of the user, being even possible that his/her interest increases over time from the purchase. Some existing approaches focus only on the first factor, missing the point that the influence of time can be very different for different users. To solve this limitation, we present a filtering strategy that exploits the semantics formalized in an ontology in order to link items(and their features) to time functions. The novelty lies within the fact that the shapes of these functions are corrected by gy temporal curves built from the consumption stereotypes into which each user fits best. Our preliminary xperiments involving real users have revealed significant improvements of recommendation precision with regard to previous time-driven filtering approaches. c 2011 Elsevier Ltd. All rights reserved Discovering products that meet the needs of the consumers Obviously, keeping the users'satisfad re crucial in such competitive environments as online shopping. to adapt the selection of items as their interests evolve over time. Recommender systems assist in advertising tasks by automati- For many years, in most of the existing filtering strategies, data cally selecting the most appropriate items for each user as per hi her personal interests and preferences(Adomavicius and Tuzhilin, process, weighing equally the ratings given by the users at 2005). Research in recommender systems started back in the different times. Later, some researchers proposed time-aware early 1990s, but the greatest advances have been due to the approaches that made the last observations more significant than irruption of recent technologies like those of the Semantic Web the older ones, which means assuming that a user's interest in a (Berners-Lee et al, ) It has been proved that semantics-based product always decreases from the moment of the last purchase recommender systems can outperform previous approaches by (see examples in Maloof and Michalski, 2000: Schwab et al, 2001 exploiting two main elements: Duen-Ren and Ya-Yueh, 2005: Ding and Li, 2005: Lee and Park, 2009). This may be true in certain areas of application, such as a knowledge base typically an ontology that represents personalized programming guides that recommend Tv programs semantic features or attributes of the available items and to the users. Notwithstanding, the interest in (or the need for) filtering strategies based on semantic reasoning techniques that commercial products in general may actually increase or vary in discover relevant relationships between the users'preferences diverse forms over time. For example, if a user has just bought a and the items to be recommended (see examples in Hung, 2005 dishwasher, it is foreseeable that he/she will not need another Middleton et al, 2004: Yuan and Cheng, 2004: Blanco-Fernandez one until the average lifetime of such appliances has passed; et al. 2008: Pazos-Arias Jose et al. 2008: Blanco-Fernandez therefore, the interest estimations should follow an increasing et al. 2010). function, and any recommender system should prioritize other may vary along the year, while the interest in books and music nay remain constant and school equipment may have a peak funded by the ministerio de educacion y Ciencia(Gobierno de espana he beginning of the academic yeal sponding author Fax: +34 986812116 The main research contribution of this paper is an improve- address: yolanda@det vigo. es(Y. Blanco-Fernandez). ment to the current filtering strategies, aimed at increasing the 0952-1976s- er e 2011 Elsevier Ltd. All rights reserved. Please cite this article as: Blanco-Fernandez, Y, et al, An improvement for semantics-based recommender systems grounded on attaching temporal information. Engineering Applications of Artificial Intelligence (2011). doi: 10.1016/.engappai 2011.02.020
An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles$ Yolanda Blanco-Ferna´ndez , Martı´n Lo´pez-Nores, Jose´ J. Pazos-Arias, Jorge Garcı´a-Duque ETSE Telecomunicacio´n, Campus Universitario, Vigo. 36310, Spain article info Article history: Received 15 June 2010 Received in revised form 24 February 2011 Accepted 25 February 2011 Keywords: Personalization Recommender systems Semantic reasoning Time-aware filtering Ontology Consumption stereotypes abstract Recommender systems in online shopping automatically select the most appropriate items to each user, thus shortening his/her product searching time in the shops and adapting the selection as his/her particular preferences evolve over time. This adaptation process typically considers that a user’s interest in a given type of product always decreases with time from the moment of the last purchase. However, the necessity of a product for a user depends on both the nature of the own item and the personal preferences of the user, being even possible that his/her interest increases over time from the purchase. Some existing approaches focus only on the first factor, missing the point that the influence of time can be very different for different users. To solve this limitation, we present a filtering strategy that exploits the semantics formalized in an ontology in order to link items (and their features) to time functions. The novelty lies within the fact that the shapes of these functions are corrected by temporal curves built from the consumption stereotypes into which each user fits best. Our preliminary experiments involving real users have revealed significant improvements of recommendation precision with regard to previous time-driven filtering approaches. & 2011 Elsevier Ltd. All rights reserved. 1. Introduction Discovering products that meet the needs of the consumers are crucial in such competitive environments as online shopping. Recommender systems assist in advertising tasks by automatically selecting the most appropriate items for each user as per his/ her personal interests and preferences (Adomavicius and Tuzhilin, 2005). Research in recommender systems started back in the early 1990s, but the greatest advances have been due to the irruption of recent technologies like those of the Semantic Web (Berners-Lee et al.,). It has been proved that semantics-based recommender systems can outperform previous approaches by exploiting two main elements: a knowledge base – typically an ontology – that represents semantic features or attributes of the available items, and filtering strategies based on semantic reasoning techniques that discover relevant relationships between the users’ preferences and the items to be recommended (see examples in Hung, 2005; Middleton et al., 2004; Yuan and Cheng, 2004; Blanco-Ferna´ndez et al., 2008; Pazos-Arias Jose´ et al., 2008; Blanco-Ferna´ndez et al., 2010). Obviously, keeping the users’ satisfaction high requires means to adapt the selection of items as their interests evolve over time. For many years, in most of the existing filtering strategies, data collection about the users’ interests was regarded as a static process, weighing equally the ratings given by the users at different times. Later, some researchers proposed time-aware approaches that made the last observations more significant than the older ones, which means assuming that a user’s interest in a product always decreases from the moment of the last purchase (see examples in Maloof and Michalski, 2000; Schwab et al., 2001; Duen-Ren and Ya-Yueh, 2005; Ding and Li, 2005; Lee and Park, 2009). This may be true in certain areas of application, such as personalized programming guides that recommend TV programs to the users. Notwithstanding, the interest in (or the need for) commercial products in general may actually increase or vary in diverse forms over time. For example, if a user has just bought a dishwasher, it is foreseeable that he/she will not need another one until the average lifetime of such appliances has passed; therefore, the interest estimations should follow an increasing function, and any recommender system should prioritize other products for some time. Likewise, the interest for seasonal clothes may vary along the year, while the interest in books and music may remain constant and school equipment may have a peak at the beginning of the academic year. The main research contribution of this paper is an improvement to the current filtering strategies, aimed at increasing the Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/engappai Engineering Applications of Artificial Intelligence 0952-1976/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.engappai.2011.02.020 $Work funded by the Ministerio de Educacio´n y Ciencia (Gobierno de Espan˜a) research project TIN2010-20797. Corresponding author. Fax: þ34 986812116. E-mail address: yolanda@det.uvigo.es (Y. Blanco-Ferna´ndez). Please cite this article as: Blanco-Ferna´ndez, Y., et al., An improvement for semantics-based recommender systems grounded on attaching temporal information.... Engineering Applications of Artificial Intelligence (2011), doi:10.1016/j.engappai.2011.02.020 Engineering Applications of Artificial Intelligence ] (]]]]) ]]]–]]]
ARTICLE IN PRESS fective combine the strengths of content-based and nding items similar to the ones ering two items similar if the att Research in number of pract the issues of It to note that all of were time-unaware, inasmuch ased recommender systems grounded on doi:10.1016j.engappai.2011.02.020
effectiveness of semantics-based recommender systems in online shopping. The basic assumption is that the influence of time can be radically different not only for different types of items as explained above, but also for different users. For instance, whereas car tires typically have a lifetime of 6 years for average drivers, it is expectable that taxi drivers or users interested in car tuning and motor sports need more frequent replacements (say, every 6 months). Analogously, it makes sense not to recommend dolls for some time after an average user has bought one, but the same is not true for doll collectors. Briefly speaking, our new approach makes tailor-made selections of items by exploiting the semantics formalized in an ontology to link items (and their features) to time functions, whose shapes are corrected by considering the preferences of like-minded individuals and the effects of time in their purchasing behaviors. The paper is organized as follows. Section 2 includes a review of recommender systems literature to highlight the differences between the management of time in previous works and in our new filtering strategy. Next, Section 3 details the main parts of our personalization framework, while in Section 4 we focus on the algorithmic internals of our time-aware filtering strategy. Section 5 presents the results of experiments we have carried out (with real users) to assess the personalization quality achieved by the new filtering strategy in comparison with existing approaches. Finally, Section 6 provides a summary of conclusions and the motivation of our ongoing work. 2. Related work Research in recommender systems is hectic nowadays, in an attempt to address the many new questions raised by the growing number of practical applications. Next, we provide an overview of the milestones in recommenders history, and thereafter focus on the issues of producing time-aware recommendations in online shopping, which remain practically unexplored in literature. 2.1. Background on recommender systems Given a set of items, the goal of a recommender system is to identify the most suitable ones according to the information stored in a user’s profile by adopting diverse filtering strategies. The first strategies merely looked at demographic information (e.g. age, gender or marital status) to recommend items that had interested other users with similar data. The results so obtained tend to be imprecise and fail to reflect changes of the user preferences over time (because personal data are often stable for long periods). This problem was addressed by content-based filtering, that looks for items similar to others that gained the user’s interest in the past (Adomavicius and Tuzhilin, 2005; Dias et al., 2008). This strategy is easy to adopt, but bears a problem of overspecialization: the recommendations tend to be repetitive for considering that a user will always appreciate the same kind of items. Furthermore, the limited data available about new users makes the first results highly inaccurate. To tackle these problems, the scientific community came up with collaborative filtering, that proceeds by evaluating not only the profile of the target user (the one who will receive the recommendations), but also those of users with similar interests (his/her neighbors) (Schafer et al., 2001; Montaner et al., 2003). This approach can solve the lack of diversity in the recommendations, but faces problems like the sparsity when the number of items is high (which makes it hard to find users with similar evaluations for the same items) or the treatment given to users whose preferences are dissimilar to the majority (the gray sheep). There exist hybrid approaches that attempt to neutralize the weaknesses and combine the strengths of content-based and collaborative filtering, e.g. recommending items similar to the ones listed in the user’s profile, but considering two items similar if the individuals who show interest in the one tend to be interested in the other (Papagelis and Plexousakis, 2005; Burke, 2002; Li et al., 2005). Both in content-based filtering and collaborative filtering, the user profiles are typically initialized from stereotypes which are mechanisms that provide general descriptions for a set to similar users (Rich, 1979). Actually, stereotypes allow to build models of individual users on the basis of a small amount of information about them (e.g. age, occupation, lifestyle, etc.). As described in Montaner et al. (2003), Shani et al. (2007), Kobsa et al. (2001) and Krulwich (1997), stereotypes have also been widely adopted in diverse filtering strategies for the selection of the most appropriate recommendations for each user. Regardless of the filtering strategy, it is noticeable that most of the recommender systems have relied on syntactic matching techniques, that relate items by looking for common words in their attached metadata. Even though there exist plenty of different approaches, they all miss much knowledge during the personalization process, because they are unable to reason about the meaning of the metadata. A syntactic approach is also a source of overspecialization, because the recommendations so computed can only include items very similar to those the users already know (Adomavicius and Tuzhilin, 2005). To go one step beyond in personalization quality and diversity, research is now focused on applying techniques from the Semantic Web, that allow to gain insight into the meaning of words. The key here lies within the use of ontologies to describe and interrelate items and their attributes by means of class hierarchies and properties (Staab and Studer, 2004). Thus, many authors have enhanced the traditional filtering strategies with semantic reasoning mechanisms, to discover the items that best match the preferences of each user by reasoning about their semantic descriptions. Hung (2005), proposed a recommender system for one-to-one marketing based on a taxonomy of products, revealing the advantages of such semantics when it came to providing instant online recommendations and identifying potential customers upon release of a new product. Middleton et al., 2004 explored a novel ontological approach to user profiling within semantics-based recommender systems, coping with the problem of recommending academic research papers; the experiments showed that profile visualization and feedback outperformed previous user modelling approaches, which led the authors to conclude that the semantics captured by their ontological approach made the profiles easier to understand. The authors of Yuan and Cheng (2004) investigated analogy structures between heterogeneous products (i.e., products with different properties) to recommend items that are disparate from others the users had purchased, using what they called an ontology-driven coupled clustering algorithm. We have previously explored the benefits of semantics-based recommender systems in other domains. In Blanco-Ferna´ndez et al. (2008), we proposed an ontology-driven recommendation system to select the most appealing TV programs for the users. In PazosArias Jose´ et al. (2008), we incorporated a similar semanticsenhanced approach into a t-learning platform to recommend personalized educational courses according to the users’ preferences and previous knowledge. Finally, in Blanco-Ferna´ndez et al. (2010), we exploited the benefits of semantics-driven reasoning in a tourism recommender system. 2.2. Background on time-aware filtering approaches For the purposes of this paper, it is important to note that all of the abovementioned approaches were time-unaware, inasmuch 2 Y. Blanco-Ferna´ndez et al. / Engineering Applications of Artificial Intelligence ] (]]]]) ]]]–]]] Please cite this article as: Blanco-Ferna´ndez, Y., et al., An improvement for semantics-based recommender systems grounded on attaching temporal information.... Engineering Applications of Artificial Intelligence (2011), doi:10.1016/j.engappai.2011.02.020
ARTICLE IN PRESS Y. Blanco-Ferandez et al. /E they did not include any mechanisms to take into account n a are new time-aware filtering strategy vill be presented in Section 4. concerns in the domain ontology commendation of items in the we need an ontology that f formalizes is domain. The creation of items. e also oute is )to re purchased item class pliances). The the success a function that minded time of purchase. ith the instant when able 1 3.0 d once its lifetime T This section lization framework:the time functions attached to its nodes; the and the stereotypes users; and the group correc Please cite this article a ed recommender systems grounded on attaching temporal information.... E doi:10.1016jengappai.211.02.020
as they did not include any mechanisms to take into account the influence of time on the user’s interests, preferences and needs. The first attempts to consider this effect consisted in introducing gradual forgetting functions, to make the most recent observations more significant than the older ones during the computation of recommendations (Koychev, 2000; Maloof and Michalski, 2000; Schwab et al., 2001; Duen-Ren and Ya-Yueh, 2005). Specifically, when a new item was added to a user’s profile, its weight was set to 1 and the values of the other items were decreased. Most often, this was done as per a constant decay rate, but some authors considered different rates for different item classes and even supplementing recency with other information. Indeed, the authors of Ding and Li (2005) presented an approach to trace changes in the purchase interests of each user and thereby compute personalized decay factor. In Ding et al. (2006), the same authors extended their initial approach by using weights for items based on their expected accuracy on the future preferences and making decisions based on data arrival time. More recently, the authors of Lee and Park (2009) motivated the exploitation of other temporal information to improve the accuracy of a collaborative filtering strategy. Specifically, they presented an approach that involved item launch times (indicating the age of the items), purchase times (denoting the age of the users’ preferences) and the time difference between both (representing the temporal gap between when an item is released and when a user purchases it). Although it is clearly more sophisticated, this approach ultimately comes down to the same assumption of gradual forgetting, i.e., that the interest of the user in an item always decreases from the moment of the last purchase. In Blanco-Ferna´ndez et al. (2008), we presented the first timeaware filtering approach that reckoned the fact that, in general applications of recommender systems (and, particularly, in online shopping), the interest in (or the need for) certain items may actually increase over time. In that paper, we harnessed the conceptualization provided by an ontology to link the classes and attributes of the items to time functions that modelled dependences with regard to absolute dates or purchase times. Although the experimental results showed that this approach could outperform time-unaware filtering proposals (see BlancoFerna´ndez et al., 2008), we guessed that the effectiveness of the recommender system could improve even further by considering that the influence of time can be radically different for different users (recall the motivation examples given in Section 1). Therefore, in this paper, we shall enhance the approach presented in Blanco-Ferna´ndez et al. (2008) to involve not only item classes but also user preferences in modelling time dependences. To this aim, we propose to modify the shape of the default time functions (i.e., the ones defined in the ontology for average users) by means of an adaptive group correction, built from consumption stereotypes that cluster together users who share some of their preferences. We do not consider individual corrections – as they did in Ding and Li (2005) to tailor decay rates – because it would be unfeasible to gather sufficient information from every single user to accurately characterize his/her potential interest in all item classes over time. Instead, it makes more sense to consider the success or failure of the recommendations made to likeminded individuals. 3. Our personalization framework This section describes the main elements of our new personalization framework: the domain ontology and the parameterized time functions attached to its nodes; the individual user profiles and the stereotypes that model the preferences of groups of users; and the group corrections that modify the default time dependence curves. The new time-aware filtering strategy enabled by these elements will be presented in Section 4. 3.1. Including time concerns in the domain ontology Since we are considering the recommendation of items in the scope of online shopping, we need an ontology that formalizes typical concepts and relationships in this domain. The creation of such an ontology is problematic due to the high degree of specificity (that leads to a very large number of concepts) and the need for timely maintenance, owing to the continuous innovations that take place in the domain of products and services. Therefore, we did not intend to create an ontology covering all possible types of items, but rather to use one that could be easily extracted from some of the classification standards available for industrial products and services. To this aim, we looked at standards like UNSPSC,1 eCl@ss,2 eOTD3 and the RosettaNet Technical Dictionary,4 which reflect some level of community consensus and contain multiple definitions of hierarchically organized concepts. Finally, we chose eCl@ss as the main input for creating the domain ontology, due to the reasons of completeness, balance and maintenance discussed in Hepp et al. (2007). More specifically, we have borrowed from eClassOWL – the OWL ontology for products and services developed by Hepp (2006) – many concepts referred to categorizations of commercial products, and we have also defined new properties and classes to accommodate some missing features. Along with the multiple hierarchies of classes that serve to categorize the commercial products and their attributes, the ontology contains labeled properties joining each item to its attributes and seeAlso properties to link strongly related items. The ontology was populated by retrieving information from multiple online retailers. A brief excerpt is depicted in Fig. 1, where classes, items and attributes are denoted by gray ellipses, white squares and white ellipses, respectively. Our new approach to time-aware filtering starts out by associating parameterized time functions to item classes and attributes, in order to model the variation of the potential interest of each type of product or any of its features with regard to absolute dates or purchase times. Most commonly, the specific time function associated to a given item is chosen based on the marketing criteria handled by the providers. We handle functions with diverse shapes, including combinations of constant, linear, exponential, sinusoidal, parabolic, hyperbolic and elliptic segments, with values between 0 and 1. As a rule of thumb, low values are intended to prevent the recommendation of the items, whereas high values are intended to promote them. We also require valid functions to take the maximum value (1) at some point, corresponding to the time an item or an attribute is potentially most interesting for a general audience. Next, we shall exemplify some of the functions (see Table 1) to explain how the time-aware filtering works: Monotonically increasing function. Some items are purchased sporadically due to their long average lifetime (e.g. consumer electronics devices, vehicles and household appliances). The interest for such products can be modeled by a function that grows (linearly or exponentially) from the time of purchase. The zero value of the function coincides with the instant when the user bought the product (denoted by T1 in Table 1), whereas the maximum value 1 is reached once its lifetime has expired (T2). 1 http://www.unspsc.org 2 http://www.eclass-online.com 3 http://registry.eccma.org/eotd 4 http://www.rosettanet.org Y. Blanco-Ferna´ndez et al. / Engineering Applications of Artificial Intelligence ] (]]]]) ]]]–]]] 3 Please cite this article as: Blanco-Ferna´ndez, Y., et al., An improvement for semantics-based recommender systems grounded on attaching temporal information.... Engineering Applications of Artificial Intelligence (2011), doi:10.1016/j.engappai.2011.02.020
ARTICLE IN PRESS Y. Blanco-Fernandez et aL/ Engineering Applications of Artificial Intelligence i( )In rdf: subClassof ObjectProp rdf: Id rof: ld rof ld rdf- ld (,mw -oHl, The simplest time functions used in our filtering strategy Function Chart Associated products Items that are purchased sporadically due to their long average lifetime. Monotonically decreasing Products that are useful during a limited period and whose utility decreases over time. Rectangle Items that may be repeatedly purchased over a period of time. Constant Products that the user may purchase daily. Monotonically decreasing function. Some seasonal items (eg. Constant function. A constant(time-unaware) function can be wimming pool supplies)are useful during a limited period linked to products that the user may purchase daily, such as and their utility decreases over time. In such cases, the oks or personal hygiene items. temporal dependence can be modeled with a function that takes the maximum value up to the beginning of the season As we shall explain later, the time function to use for a specifi (instant T, in Table 1)and decreases monotonically (linearly or item during the recommendation process is computed from the exponentially) afterwards. The zero value is reached once the functions linked to the classes it belongs to, and also from the seasonal period has ended (instant T2) functions linked to its attributes. By default, attributes are associated a bound eriod of time. This is the case, for example, of nougat the description of the rectangle function),while each class inherits the Rectangle function. A rectangle function (see Table 1)can be to a constant function of value 1, which can be modified as per those months, the zero value of the function prevents su ible to disregard the inheritance process by manually assig gning products from appearing in any recommendation specific functions to the classes and attributes of specific items. Please cite this article as: Blanco-Fernandez, Y. et al, An improvement for semantics-based recommender systems grounded on attaching temporal information. Engineering Applications of Artificial Intelligence(2011), doi: 10. 1016j-engappai 2011.02.020
Monotonically decreasing function. Some seasonal items (e.g. swimming pool supplies) are useful during a limited period and their utility decreases over time. In such cases, the temporal dependence can be modeled with a function that takes the maximum value up to the beginning of the season (instant T1 in Table 1) and decreases monotonically (linearly or exponentially) afterwards. The zero value is reached once the seasonal period has ended (instant T2). Rectangle function. A rectangle function (see Table 1) can be bound to products that may be repeatedly purchased during a given period of time. This is the case, for example, of nougat bars, which are mainly available around Christmas—out of those months, the zero value of the function prevents such products from appearing in any recommendation. Constant function. A constant (time-unaware) function can be linked to products that the user may purchase daily, such as books or personal hygiene items. As we shall explain later, the time function to use for a specific item during the recommendation process is computed from the functions linked to the classes it belongs to, and also from the functions linked to its attributes. By default, attributes are associated to a constant function of value 1, which can be modified as per marketing criteria (remember examples about nougat bars given in the description of the rectangle function), while each class inherits the temporal behavior of its immediate superclass. In any case, it is also possible to disregard the inheritance process by manually assigning specific functions to the classes and attributes of specific items. Fig. 1. A little excerpt from our ontology. Table 1 The simplest time functions used in our filtering strategy. Function Chart Associated products Monotonically increasing Items that are purchased sporadically due to their long average lifetime. Monotonically decreasing Products that are useful during a limited period and whose utility decreases over time. Rectangle Items that may be repeatedly purchased over a period of time. Constant Products that the user may purchase daily. 4 Y. Blanco-Ferna´ndez et al. / Engineering Applications of Artificial Intelligence ] (]]]]) ]]]–]]] Please cite this article as: Blanco-Ferna´ndez, Y., et al., An improvement for semantics-based recommender systems grounded on attaching temporal information.... Engineering Applications of Artificial Intelligence (2011), doi:10.1016/j.engappai.2011.02.020
ARTICLE IN PRESS Y. Blanco-Ferndndez et aL Engineering Applications of Artificial Intelligence i (l)Il a Fig. 2. Computation of group correction:(a)the starting zero function; (b)a sample pulse train and (c)the resulting group correction. 3. 2. Building group corrections from user profiles and stereotypes between those of the books b, and b2, whereas Books Up to this point, everything is independent of the individual preferences and needs of any user(as it was in blanco-Fernandez Our stereotypes take the same form as the individual profiles, et al. 2008), so we need additional artifacts to incorporate the though completely void of information that might serve to sers'personal interests into the filtering process. To this aim, for identify individual users. In other words, a stereotype is an the reasons explained at the end of Section 2. 2, we do not proceed excerpt from the ontology with attached DOls. These DOls can dividually, but rather with groups of users who may be be anonymously updated from the given/inferred ratings of the clustered together as per some of their preferences. Next, we users, just knowing their degree of membership to the stereotype explain the structure of the user profiles we have been in question (a number whose computation will be explained handling to capture the knowledge available about the user, and later). Actually, the feedback messages include (i) the user's then introduce a notion of stereotypes to characterize the degree of membership to a given stereotype, (if)the rating given preferences of groups of users(potential audiences for certain to(or inferred for)an item, and (ii)the time when the rating was given/inferred. We use that information to build and maintain In our work, a user's profile stores various data, including a function called group correction, intended to modify the default record of the items he/she bought in the past, the classes and time dependence that results for an item from its classes and attributes that describe those items in the ontology and the time attributes. Starting with the zero function that is assumed by of the last purchase. Furthermore each item is linked to a number default (ie, in the absence of feedback ) we compute one group Don)of the user in it(-1 represents the greatest disliking: 1 the function of time, using the procedure depicted in F1g. 2. s as a between- and 1 that measures the degree of interest(hereafter correction gc(Cm, Sj, t) for each class Cm of a stereotype greatest liking). In formal notation, this number is denoted by DOI(,U)=xE[-1, 1]and represents the interest of the user U in First, we record the ratings received for items belonging to the the item i. DOl indexes may be given explicitly by the user, or inferred indirectly by monitoring his/her interaction with the Second, we build a pulse train by averaging the ratings of each recommendations(e.g. if a user decides to buy a recommended instant, weighed by the degrees of membership to the ster item, then we can assume a very positive rating for it-see lopez type S of the users who provided them Nores et al. 2010 for more examples). In any case, the DOls of the Finally, we approximate the pulse train by a natural smoothing ems propagate to attributes and classes as follows uDan o that each piece of feedback has an The Dol of an attribute is calculated by averaging the ratings of instant for which it was issued. ot only at the specific time effect over a pel e resulting curve is trimmed the items that are joined to it in the ontology. In the excerpt ween -1 and 1 from ontology depicted in Fig. 1, for example, the attributes "fall of the Bastille"and"Old Regime Crisis"inherit the ratings We devised group corrections as an additive adjustment of the given by the user to books b1 and b2(0.8 an spectively ) items'time functions. so the result of the sum At the bottom of the hierarchy, the dol of a leaf class is 0 and 1 and normalized to take the maximum value 1 - yields calculated by averaging the ratings of all the items that belong another time function. As shown in Fig. 3, the corrections can to it Upwards, each class averages the DOls of its child classes, completely modify the shape of the temporal dependence curves, as assuming a neutral rating (of value 0)for unrated classes. Fc needed to reckon the fact that the purchasing behaviors of certain example, History"in Fig. 1 receives a Dol of 0.9, halfway people may be radically different from those of the majorit Please cite this article as: Blanco-Fernandez, Y, et al, An improvement for semantics-based recommender systems grounded on attaching temporal information. Engineering Applications of Artificial Intelligence (2011). doi: 10.1016/.engappai 2011.02.020
3.2. Building group corrections from user profiles and stereotypes Up to this point, everything is independent of the individual preferences and needs of any user (as it was in Blanco-Ferna´ndez et al., 2008), so we need additional artifacts to incorporate the users’ personal interests into the filtering process. To this aim, for the reasons explained at the end of Section 2.2, we do not proceed individually, but rather with groups of users who may be clustered together as per some of their preferences. Next, we shall explain the structure of the user profiles we have been handling to capture the knowledge available about the user, and then introduce a notion of stereotypes to characterize the preferences of groups of users (potential audiences for certain products). In our work, a user’s profile stores various data, including a record of the items he/she bought in the past, the classes and attributes that describe those items in the ontology and the time of the last purchase. Furthermore, each item is linked to a number between 1 and 1 that measures the degree of interest (hereafter DOI) of the user in it (1 represents the greatest disliking; 1 the greatest liking). In formal notation, this number is denoted by DOIði,UÞ ¼ xA½1,1 and represents the interest of the user U in the item i. DOI indexes may be given explicitly by the user, or inferred indirectly by monitoring his/her interaction with the recommendations (e.g. if a user decides to buy a recommended item, then we can assume a very positive rating for it—see Lo´pezNores et al., 2010 for more examples). In any case, the DOIs of the items propagate to attributes and classes as follows: The DOI of an attribute is calculated by averaging the ratings of the items that are joined to it in the ontology. In the excerpt from ontology depicted in Fig. 1, for example, the attributes ‘‘Fall of the Bastille’’ and ‘‘Old Regime Crisis’’ inherit the ratings given by the user to books b1 and b2 (0.8 and 1, respectively). At the bottom of the hierarchy, the DOI of a leaf class is calculated by averaging the ratings of all the items that belong to it. Upwards, each class averages the DOIs of its child classes, assuming a neutral rating (of value 0) for unrated classes. For example, ‘‘History’’ in Fig. 1 receives a DOI of 0.9, halfway between those of the books b1 and b2, whereas ‘‘Books’’ receives 0.45 as the average of ‘‘History’’ and ‘‘Sciences’’. Our stereotypes take the same form as the individual profiles, though completely void of information that might serve to identify individual users. In other words, a stereotype is an excerpt from the ontology with attached DOIs. These DOIs can be anonymously updated from the given/inferred ratings of the users, just knowing their degree of membership to the stereotype in question (a number whose computation will be explained later). Actually, the feedback messages include (i) the user’s degree of membership to a given stereotype, (ii) the rating given to (or inferred for) an item, and (iii) the time when the rating was given/inferred. We use that information to build and maintain a function called group correction, intended to modify the default time dependence that results for an item from its classes and attributes. Starting with the zero function that is assumed by default (i.e., in the absence of feedback), we compute one group correction gcðCm,Sj,tÞ for each class Cm of a stereotype Sj as a function of time, using the procedure depicted in Fig. 2: First, we record the ratings received for items belonging to the class Cm in the different time instants. Second, we build a pulse train by averaging the ratings of each instant, weighed by the degrees of membership to the stereotype Sj of the users who provided them. Finally, we approximate the pulse train by a natural smoothing spline (Eubank, 1999), so that each piece of feedback has an effect over a period of time and not only at the specific time instant for which it was issued. The resulting curve is trimmed between 1 and 1. We devised group corrections as an additive adjustment of the items’ time functions, so the result of the sum – trimmed between 0 and 1 and normalized to take the maximum value 1 – yields another time function. As shown in Fig. 3, the corrections can completely modify the shape of the temporal dependence curves, as needed to reckon the fact that the purchasing behaviors of certain people may be radically different from those of the majority. Fig. 2. Computation of group correction: (a) the starting zero function; (b) a sample pulse train and (c) the resulting group correction. Y. Blanco-Ferna´ndez et al. / Engineering Applications of Artificial Intelligence ] (]]]]) ]]]–]]] 5 Please cite this article as: Blanco-Ferna´ndez, Y., et al., An improvement for semantics-based recommender systems grounded on attaching temporal information.... Engineering Applications of Artificial Intelligence (2011), doi:10.1016/j.engappai.2011.02.020