A. K. Milicevic et al Fig. 1 Conceptual model of a T Users elaborative tagging system Marlow et al. 2006) t7, t2, t5 t1. t2, t3 t8 t1,t8,t7 t1, t8, t9 t1, t8, t7 number of tags that all refer to the same object (item), because users might independently se very distinct tags for the same content. The narrow folksonomy, which a tool like Flicki represents, provides benefit in tagging objects that are not easily searchable or have no other means of using text to describe or find the object. The narrow folksonomy is done by one or few people providing tags that the person uses to get back to that information. The tags, unlike in the broad folksonomy, are singular in nature. The same tag cannot be associated with a single object multiple times; in other words, the creator or publisher of an object is often the person who creates the first tags(unlike in broad folksonomies), and the option to ag may be even restricted to that person. After all, a much smaller number of tags for one and the same object can be identified in a narrow folksonomy 3 A model for tagging activities Social tagging systems allow their users to share their tags of particular resources. Each tag serves as a link to additional resources tagged in the same way by other users(Marlow et al. 2006). Certain resources may be linked to each other; at the same time, there may be relationships between users according to their own social interests, so the shared tags of a folksonomy come to interconnect the three groups of protagonists in social labeling systems Many researchers(Mika 2005: Harry et al. 2006; Ciro et al. 2007) suggested a triparti model that represents the Tagging Proces where U is the set of users who participate in a tagging activity, T is the set of available tags and I is the set of items being tagged Figure I shows a conceptual model for social tagging system where users and items are connected through the tags they assign. In this model, assign t specific item; tags are represented as typed edges connecting users and items. Items may be connected to each other(e. g, as links between web pages)and users may be associated by a social network, or sets of affiliations(e. g, users that work for the 2 spr
192 A. K. Milicevic et al. Fig. 1 Conceptual model of a collaborative tagging system (Marlow et al. 2006) t7, t2,t5 t1, t2,t3 t1, t2,t3 t1, t8,t9 t1, t8,t7 t1, t8,t7 t1, t8,t7 Items Users Tags number of tags that all refer to the same object (item), because users might independently use very distinct tags for the same content. The narrow folksonomy, which a tool like Flickr represents, provides benefit in tagging objects that are not easily searchable or have no other means of using text to describe or find the object. The narrow folksonomy is done by one or a few people providing tags that the person uses to get back to that information. The tags, unlike in the broad folksonomy, are singular in nature. The same tag cannot be associated with a single object multiple times; in other words, the creator or publisher of an object is often the person who creates the first tags (unlike in broad folksonomies), and the option to tag may be even restricted to that person. After all, a much smaller number of tags for one and the same object can be identified in a narrow folksonomy. 3 A model for tagging activities Social tagging systems allow their users to share their tags of particular resources. Each tag serves as a link to additional resources tagged in the same way by other users (Marlow et al. 2006). Certain resources may be linked to each other; at the same time, there may be relationships between users according to their own social interests, so the shared tags of a folksonomy come to interconnect the three groups of protagonists in social labeling systems: Users, Items, and Tags. Many researchers (Mika 2005; Harry et al. 2006; Ciro et al. 2007) suggested a tripartite model that represents the Tagging Process: Tagging : (U, T, I) (1) where U is the set of users who participate in a tagging activity, T is the set of available tags and I is the set of items being tagged. Figure 1 shows a conceptual model for social tagging system where users and items are connected through the tags they assign. In this model, users assign tags to a specific item; tags are represented as typed edges connecting users and items. Items may be connected to each other (e.g., as links between web pages) and users may be associated by a social network, or sets of affiliations (e.g., users that work for the same company). 123
Social tagging in recommender systems Examination( Golder and Huberman 2005)of the collaborative tagging system, such as Delicious, has revealed a rich variety in the ways in which tags are used, regularities in user tivity, tag frequencies, and bursts of popularity in bookmarking, as well as a remarkable stability in the relative proportions of tags within a given url. Tags may be used to identify the topic of a resource using nouns and proper nouns (i.e photo, album, photographer) To classify the type of resource (i.e. book, blog, article, review, event) To denote the qualities and characteristics of the item(i.e funny, useful, cool) A subset of tags, such as myfavourites, mymusic and myphotos reflect a notion of self reference Some tags are used by individuals for task organisation(e.g to read, job search, and to Time is an important factor in considering collaborative tagging systems, in fact definitions and relationships among tags could vary over time. For certain users, the number of tags can become stable over time, while for others, it keeps growing. There are three hypotheses about tags behavior over time(Harry et al. 2006) a. Tags convergence: the tags assigned to a certain Web resource tend to stabilize and to become the majority. b. Tags divergence: tag-sets that don't converge to a smaller group of more stable tags, and where the tag distribution continually changes c. Tags periodicity: after one group of users tag some local optimal tag-set, another group uses a divergent set but, after a period of time the new group,'s set becomes the new local optimal tag-set. This process may repeat and so lead to convergence after a period of instability, or it may act like a chaotic attractor. 4 Tag-based recommender systems Recommender systems in general recommend interesting or personalized information objects to users based on explicit or implicit ratings. Usually, recommender systems predict rat ings of objects or suggest a list of new objects that the user hopefully will like the most The approaches of profiling users with user-item rating matrix and keywords vectors are widely used in recommender systems. However, these approaches are used for describing two-dimensional relationships between users and items. In tag recommender systems the rec- mendations are, for a given user u E U and a given resourcer E R, a set T(u, r)C Tof tags. In many cases, T(u, r)is computed by first generating a ranking on the set of tags according to some quality or relevance criterion, from which then the top n elements are selected (Jaschke et al. 2007) Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation is one of the most successful recommendation tech niques to date. However, collaborative filtering recommendation becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social tagging websites not only tells what a user likes, but also why he or she likes it. the remainder of this section, we first describe the proposed extension with integrating tags information to improve recommendation quality. We then present well-known recom- mendation algorithms for developing Tag-Based Recommender Systems. Probabilistic latent
Social tagging in recommender systems 193 Examination (Golder and Huberman 2005) of the collaborative tagging system, such as Delicious, has revealed a rich variety in the ways in which tags are used, regularities in user activity, tag frequencies, and bursts of popularity in bookmarking, as well as a remarkable stability in the relative proportions of tags within a given url. • Tags may be used to identify the topic of a resource using nouns and proper nouns (i.e. photo, album, photographer). • To classify the type of resource (i.e. book, blog, article, review, event). • To denote the qualities and characteristics of the item (i.e. funny, useful, cool). • A subset of tags, such as myfavourites, mymusic and myphotos reflect a notion of self reference. • Some tags are used by individuals for task organisation (e.g. to read, job search, and to print). Time is an important factor in considering collaborative tagging systems, in fact definitions and relationships among tags could vary over time. For certain users, the number of tags can become stable over time, while for others, it keeps growing. There are three hypotheses about tags behavior over time (Harry et al. 2006): a. Tags convergence: the tags assigned to a certain Web resource tend to stabilize and to become the majority. b. Tags divergence: tag-sets that don’t converge to a smaller group of more stable tags, and where the tag distribution continually changes. c. Tags periodicity: after one group of users tag some local optimal tag-set, another group uses a divergent set but, after a period of time the new group’s set becomes the new local optimal tag-set. This process may repeat and so lead to convergence after a period of instability, or it may act like a chaotic attractor. 4 Tag-based recommender systems Recommender systems in general recommend interesting or personalized information objects to users based on explicit or implicit ratings. Usually, recommender systems predict ratings of objects or suggest a list of new objects that the user hopefully will like the most. The approaches of profiling users with user-item rating matrix and keywords vectors are widely used in recommender systems. However, these approaches are used for describing two-dimensional relationships between users and items. In tag recommender systems the recommendations are, for a given user u ∈ U and a given resourcer ∈ R, a set Tˆ(u,r) ⊆ T of tags. In many cases, Tˆ(u,r)is computed by first generating a ranking on the set of tags according to some quality or relevance criterion, from which then the top n elements are selected (Jäschke et al. 2007). Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation is one of the most successful recommendation techniques to date. However, collaborative filtering recommendation becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social tagging websites not only tells what a user likes, but also why he or she likes it. In the remainder of this section, we first describe the proposed extension with integrating tags information to improve recommendation quality. We then present well-known recommendation algorithms for developing Tag-Based Recommender Systems. Probabilistic latent 123
A. K. Milicevic et al users users Item-based CF Fig 2 Extend user-item matrix by including user tags as items and item tags as users(Tso-Sutter et al. 2008) semantic analysis(PLSA), as a novel statistical technique for the analysis of two-mode and co-occurrence data, is described in Sect. 4.2. A new kind of resource sharing system, called GroupMe!, is presented in Sect. 4.3. The FolkRank algorithm developed as a folksonomy search engine by using the graph model is reported in Sect. 4.4. Section 4.5 reviews methods for tag-based profile construction with a vector of weighted tags. In later Sect. 4.6, we com- pare the method for tag-based profile construction with a single vector of weighted tags, called the naive approach, with two different approaches, one based on co-occurrence and another based on adaptation. A clustering algorithm, named WebDCC (Web Document Conceptual Clustering)is shown in Sect. 4.7. In Sect. 4.8, we give a comprehensive survey of state-of- the-art algorithms to improve music recommendation in online music recommender system, as one prominent example of companies wich offers personalized services toward users 4.1 Extension with The current recommender systems are commonly using collaborative filtering techniques which traditionally exploit only pairs of two-dimensional data. As collaborative tagging is getting more widely used, social tags as a powerful mechanism that reveal three-dimensional correlations between users-tags-items, could also be employed as background knowledge in Recommender System. The first adaptation lies in reducing the three-dimensional folksonomy to three two-dimen sional contexts: <user, tag>and <item, tag> and <user, item > This can be done by augmenting the standard user-item matrix horizontally and vertically with user and item tags correspondingly(Tso-Sutter et al. 2008). User tags, are tags that user u, uses to tag items and are viewed as items in the user-item matrix. Item tags, are tags that describe an item i, by users and play the role of users in the user-item matrix(See Fig. 2). Furthermore, instead of viewing each single tag as user or item, clustering methods can be applied to the tags suck that similar tags are grouped together. Supporting users during the tagging process is an important step towards easy-to-use applications. Consequently, different approaches have been studied in the past to find best tag recommendations for resources 2 springer
194 A. K. Milicevic et al. Fig. 2 Extend user–item matrix by including user tags as items and item tags as users (Tso-Sutter et al. 2008) semantic analysis (PLSA), as a novel statistical technique for the analysis of two-mode and co-occurrence data, is described in Sect. 4.2. A new kind of resource sharing system, called GroupMe!, is presented in Sect. 4.3. The FolkRank algorithm developed as a folksonomy search engine by using the graph model is reported in Sect. 4.4. Section 4.5 reviews methods for tag-based profile construction with a vector of weighted tags. In later Sect. 4.6, we compare the method for tag-based profile construction with a single vector of weighted tags, called the naive approach, with two different approaches, one based on co-occurrence and another based on adaptation. A clustering algorithm, named WebDCC (Web Document Conceptual Clustering) is shown in Sect. 4.7. In Sect. 4.8, we give a comprehensive survey of state-ofthe-art algorithms to improve music recommendation in online music recommender system, as one prominent example of companies wich offers personalized services toward users. 4.1 Extension with tags The current recommender systems are commonly using collaborative filtering techniques, which traditionally exploit only pairs of two-dimensional data. As collaborative tagging is getting more widely used, social tags as a powerful mechanism that reveal three-dimensional correlations between users–tags–items, could also be employed as background knowledge in Recommender System. The first adaptation lies in reducing the three-dimensional folksonomy to three two-dimensional contexts: <user, tag > and <item, tag > and <user,item >. This can be done by augmenting the standard user-item matrix horizontally and vertically with user and item tags correspondingly (Tso-Sutter et al. 2008). User tags, are tags that user u, uses to tag items and are viewed as items in the user-item matrix. Item tags, are tags that describe an item i, by users and play the role of users in the user-item matrix (See Fig. 2). Furthermore, instead of viewing each single tag as user or item, clustering methods can be applied to the tags such that similar tags are grouped together. Supporting users during the tagging process is an important step towards easy-to-use applications. Consequently, different approaches have been studied in the past to find best tag recommendations for resources. 123