《电子商务 E-business》阅读文献：Multi-Criteria Recommender Systems.pdf

2 Gediminas Adomavicius, Nikos Manouselis, YoungOk Kwon or real numbers within a certain range [4]. It is assumed that this function is not known for the whole Users x Items space but is specified only on some subset of it. Therefore, in the context of recommendation, we want for each user u E Users to be able to(a) estimate(or approximate)the utility function R(u, i) for item iEItems for which R(u, i) is not yet known, and(b)choose one or a set of items i that will maximize R(u, i),i.e vu∈ Users,i= argeliers In most recommender systems, the utility function usually considers a single- criterion value, e.g., an overall evaluation or rating of an item by a user. In recent work, this assumption has been considered as limited [2, 4, 481, because the suit- ability of the recommended item for a particular user may depend on more than one utility-related aspect that the user takes into consideration when making the choice Particularly in systems where recommendations are based on the opinion of others, the incorporation of multiple criteria that can affect the users'opinions may lead to more accurate recommendations Thus, the additional information provided by multi-criteria ratings could help to improve the quality of recommendations because it would be able to represent more complex preferences of each user. As an illustration, consider the following example. In a traditional single-rating movie recommender system, user u provides a single rating for movie i that the user has seen, denoted by R(u, i). Specifically, suppose that the recommender system predicts the rating of the movie that the user has not seen based on the movie ratings of other users with similar preferences, who are commonly referred to as"neighbors"[72]. Therefore, the ability to correctly determine the users that are most similar to the target user is crucial in order to have accurate predictions or recommendations. For example, if two users u and w have seen three movies in common, and both of them rated their overall satisfaction from each of the three movies as 6 out of 10, the two users are considered as neighbors and the ratings of unseen movies for user u are predicted using the ratings of user In contrast, in a multi-criteria rating setting, users can provide ratings on multi- ple attributes of an item. For example, a two-criterion movie recommender system allows users to specify their preferences on two attributes of a movie(e.g, story and visual effects). A user may like the story, but dislike the visual effects of a movie e.g,R(u, i)=(9, 3). If we simply use two ratings with the same weight in making application might correspond to a variety of situations in multi-rating applicate. 14 recommendations, rating their overall satisfaction as 6 out of 10 in the single-rati (9, 3),(6, 6),(4, 8), etc. Therefore, although the ratings of the overall satisfaction are stated as 6, two users may show different rating patterns on each criterion of an Item, e.g,user ratings(9, 3),(9, 3),(9, 3), and user u gives ratings (, 9),(3, 9),(3, 9)to the same three movies. This additional information on each user's preferences would help to model users' preferences more accurately, and new recommendation techniques need to be developed to take advantage of this addi- tional information. The importance of studying multi-criteria recommender systems

2 Gediminas Adomavicius, Nikos Manouselis, YoungOk Kwon or real numbers within a certain range [4]. It is assumed that this function is not known for the whole Users×Items space but is specified only on some subset of it. Therefore, in the context of recommendation, we want for each user u ∈ Users to be able to (a) estimate (or approximate) the utility function R(u,i) for item i ∈ Items for which R(u,i) is not yet known, and (b) choose one or a set of items i that will maximize R(u,i), i.e., ∀u ∈ Users,i = arg max i∈Items R(u,i) (1) In most recommender systems, the utility function usually considers a singlecriterion value, e.g., an overall evaluation or rating of an item by a user. In recent work, this assumption has been considered as limited [2, 4, 48], because the suitability of the recommended item for a particular user may depend on more than one utility-related aspect that the user takes into consideration when making the choice. Particularly in systems where recommendations are based on the opinion of others, the incorporation of multiple criteria that can affect the users’ opinions may lead to more accurate recommendations. Thus, the additional information provided by multi-criteria ratings could help to improve the quality of recommendations because it would be able to represent more complex preferences of each user. As an illustration, consider the following example. In a traditional single-rating movie recommender system, user u provides a single rating for movie i that the user has seen, denoted by R(u,i). Specifically, suppose that the recommender system predicts the rating of the movie that the user has not seen based on the movie ratings of other users with similar preferences, who are commonly referred to as “neighbors” [72]. Therefore, the ability to correctly determine the users that are most similar to the target user is crucial in order to have accurate predictions or recommendations. For example, if two users u and u ′ have seen three movies in common, and both of them rated their overall satisfaction from each of the three movies as 6 out of 10, the two users are considered as neighbors and the ratings of unseen movies for user u are predicted using the ratings of user u ′ . In contrast, in a multi-criteria rating setting, users can provide ratings on multiple attributes of an item. For example, a two-criterion movie recommender system allows users to specify their preferences on two attributes of a movie (e.g., story and visual effects). A user may like the story, but dislike the visual effects of a movie, e.g., R(u,i) = (9, 3). If we simply use two ratings with the same weight in making recommendations, rating their overall satisfaction as 6 out of 10 in the single-rating application might correspond to a variety of situations in multi-rating application: (9, 3), (6, 6), (4, 8), etc. Therefore, although the ratings of the overall satisfaction are stated as 6, two users may show different rating patterns on each criterion of an item, e.g., user u gives ratings (9, 3), (9, 3), (9, 3), and user u ′ gives ratings (3, 9), (3, 9), (3, 9) to the same three movies. This additional information on each user’s preferences would help to model users’ preferences more accurately, and new recommendation techniques need to be developed to take advantage of this additional information. The importance of studying multi-criteria recommender systems

ender Systems has been highlighted as a separate strand in the recommender systems literature [2,4, 48], and recently several recommender systems(as we present later in this chapter) have been adopting multiple criteria ratings, instead of traditional single criterion ratings. Thus, the aim of this chapter is to provide an overview of systems that use multiple criteria to support recommendation(referred to as multi-criter recommender systems, with a particular emphasis on multi-criteria rating ones The remainder of this chapter is organized as follows. First, we overview the generic recommendation problem under the prism of multi-criteria decision making (MCDM), and demonstrate the potential of applying MCDM methods to facilitate recommendation in multi-criteria settings. Second, we focus on the particular type of multi-criteria recommender systems that use multi-criteria ratings, referred to multi-criteria rating recommenders because, while it has not been extensivel researched, this type of systems has significant potential for better recommendation performance. We survey the state of the art algorithms for this type of recommender systems. Finally, research challenges and future research directions in multi-criteria recommender systems are discussed 2 Recommendation as a Multi-Criteria Decision Making Problem In order to introduce multiple criteria in the generic recommendation problem,one of the classic MCDM methodologies can be followed. To facilitate the discussion on how MCDM methods and techniques can be used when developing a recommender system, we followed the steps and notations proposed by Bernard roy (one of the 1960s pioneers in MCDM methods) in the generic modeling methodology for de- cision making problems [78]. The discussion could also follow some other generic MCDM modeling methodologies[24, 34, 96, 98], since the scope of this section is to provide some initial insight into issues that recommender systems researchers should consider when designing a multi-criteria recommender Roys[78] methodology includes four steps when analyzing a decision making I. Defining the object of decision. That is, defining the set of alternatives(items) upon which the decision has to be made and the rationale of the recommendation decision 2. Defining a consistent family of criteria. That is, identifying and specifying a set of functions that declare the preferences of the decision maker(targeted user) upon the various alternatives. These should cover all the parameters affecting the recommendation decision and be exhaustive and non -redundant 3. Developing a global preference model. That is, defining the function that synthe- sizes the partial preferences upon each criterion into a model that specifies the total preference of a decision maker regarding a candidate alternative

Multi-Criteria Recommender Systems 3 has been highlighted as a separate strand in the recommender systems literature [2, 4, 48], and recently several recommender systems (as we present later in this chapter) have been adopting multiple criteria ratings, instead of traditional singlecriterion ratings. Thus, the aim of this chapter is to provide an overview of systems that use multiple criteria to support recommendation (referred to as multi-criteria recommender systems), with a particular emphasis on multi-criteria rating ones. The remainder of this chapter is organized as follows. First, we overview the generic recommendation problem under the prism of multi-criteria decision making (MCDM), and demonstrate the potential of applying MCDM methods to facilitate recommendation in multi-criteria settings. Second, we focus on the particular type of multi-criteria recommender systems that use multi-criteria ratings, referred to as multi-criteria rating recommenders because, while it has not been extensively researched, this type of systems has significant potential for better recommendation performance. We survey the state of the art algorithms for this type of recommender systems. Finally, research challenges and future research directions in multi-criteria recommender systems are discussed. 2 Recommendation as a Multi-Criteria Decision Making Problem In order to introduce multiple criteria in the generic recommendation problem, one of the classic MCDM methodologies can be followed. To facilitate the discussion on how MCDM methods and techniques can be used when developing a recommender system, we followed the steps and notations proposed by Bernard Roy (one of the 1960s pioneers in MCDM methods) in the generic modeling methodology for decision making problems [78]. The discussion could also follow some other generic MCDM modeling methodologies [24, 34, 96, 98], since the scope of this section is to provide some initial insight into issues that recommender systems researchers should consider when designing a multi-criteria recommender. Roy’s [78] methodology includes four steps when analyzing a decision making problem: 1. Defining the object of decision. That is, defining the set of alternatives (items) upon which the decision has to be made and the rationale of the recommendation decision. 2. Defining a consistent family of criteria. That is, identifying and specifying a set of functions that declare the preferences of the decision maker (targeted user) upon the various alternatives. These should cover all the parameters affecting the recommendation decision and be exhaustive and non-redundant. 3. Developing a global preference model. That is, defining the function that synthesizes the partial preferences upon each criterion into a model that specifies the total preference of a decision maker regarding a candidate alternative

Gediminas Adomavicius, Nikos Manouselis, YoungOk Kwon 4. Selection of the decision support process. This covers the design and develop- ment of the procedure, methods, or software systems that will support a decision maker when taking a decision about the set of alternatives(items), in accordance to the results of the previous steps We briefly review these steps in separate subsections below, and mention how each of them pertains to recommender systems 2.1 Object of Decision In recommender systems, the object of decision is item i that belongs to the set of all the candidate items. The elements of this set are referred to as alternatives or actions in related literature [30- To express the rationale behind the decision, Roy [78] refers to the notion of the decision"problematics. Four types of decision problematics are identified Choice. which concerns the selection of one or more alternatives that can be considered as more appropriate from all candidate ones Sorting, which refers to the classification of the alternatives into a number of pre-defined categories, Ranking, which involves ranking all the alternatives, from the best one to the worst Description, which concerns the description of each alternative in terms of how it performs upon each criterion All four types of decision problematics can be considered valid for the recom mendation problem Choosing and recommending one or more items as more suitable for a particular user Classifying (or sorting, as Roy defines it)all available items into pre-defined categories according to their suitability, e.g., into"recommended for purchase and"recommended for viewing items Ranking all available items from the most suitable to the least suitable ones for a particular user, and presenting a ranked list of recommendations to the user, Describing how suitable a particular item is for a specific user, based on how it is evaluated upon each criterion. It corresponds to a full analysis of the item perfor mance upon all criteria, illustrating the suitability of an item for the specific user ( that is, in a personalized manner that aims to help the user to make a selection)

4 Gediminas Adomavicius, Nikos Manouselis, YoungOk Kwon 4. Selection of the decision support process. This covers the design and development of the procedure, methods, or software systems that will support a decision maker when taking a decision about the set of alternatives (items), in accordance to the results of the previous steps. We briefly review these steps in separate subsections below, and mention how each of them pertains to recommender systems. 2.1 Object of Decision In recommender systems, the object of decision is item i that belongs to the set of all the candidate items. The elements of this set are referred to as alternatives or actions in related literature [30]. To express the rationale behind the decision, Roy [78] refers to the notion of the decision “problematics.” Four types of decision problematics are identified: • Choice, which concerns the selection of one or more alternatives that can be considered as more appropriate from all candidate ones; • Sorting, which refers to the classification of the alternatives into a number of pre-defined categories; • Ranking, which involves ranking all the alternatives, from the best one to the worst; • Description, which concerns the description of each alternative in terms of how it performs upon each criterion. All four types of decision problematics can be considered valid for the recommendation problem: • Choosing and recommending one or more items as more suitable for a particular user; • Classifying (or sorting, as Roy defines it) all available items into pre-defined categories according to their suitability, e.g., into “recommended for purchase” and “recommended for viewing” items; • Ranking all available items from the most suitable to the least suitable ones for a particular user, and presenting a ranked list of recommendations to the user; • Describing how suitable a particular item is for a specific user, based on how it is evaluated upon each criterion. It corresponds to a full analysis of the item performance upon all criteria, illustrating the suitability of an item for the specific user (that is, in a personalized manner that aims to help the user to make a selection)

2. 2 Family of criteria The performance of alternatives in set Items is analyzed upon a set of criteria for each user. in order to model all their characteristics attributes effects. or conse- quences [78, 98]. In recommender systems, the criteria may refer to the multiple features of an item(often the case in content-based recommendations or to the multiple dimensions upon which the item is being evaluated/rate Any criterion c can be represented by function gc(D) that expresses the preferences of one user(therefore is user-specific), in order for the user to be able to decide between two alternatives in and 12, i.e., whether ge(i1)>g(i2), in the case that alternative iI is preferred to alternative i2, or whether g(in)=g(i2), in the case that the two alternatives are considered equivalent (i.e, perfectly substitutable for the particular user on this criterion). To be able to make rational decisions using multiple criteria, it has to be ensured that the whole set of these functions creates a consistent family of criteria [78]. A family of criteria is said to be consistent when it has the following three properties 1. Monotonic: a family of criteria is monotonic only if, for each pair of alternatives iI and i2, for which gct(i1)>gc(i2) for one criterion cI and gd(i1)=gc(2)for every other criterion c* cl, it can be assumed that alternative in is preferred to 12 2. Exhaustive: a family of criteria is exhaustive only if, for each pair of alternative in and i2, for which gc(i1)=gc(i2)upon each criterion c, we can assume that in and i2 are equivalent 3. Non-redundant: a family of criteria is non-redundant only if the removal of any one of the criteria leads to the violation of one of the other two properties In the remainder of this chapter, unless explicitly specified otherwise, we will assume that we have a consistent family of k criteria, i.e., g1, g2,..., gk. The design of a consistent family of criteria for a given recommendation application has been largely ignored in the recommender systems literature and constitutes an interesting and important problem for future research. Four types of criteria are usually found in MCDM [30] Measurable, i.e., a criterion that allows its quantified measurement upon some evaluation scale Ordinal, i.e., a criterion that defines an ordered set of acceptable values that allow its evaluation using a qualitative or a descriptive scale Probabilistic, 1.e, a criterion that uses probability distributions to represent un- certainty in its evaluation; Fuccy, i.e., a criterion whose evaluation is represented in relation to its possibility to belong in one of the intervals of a qualitative or descriptive evaluation scale From a broad perspective, a family of criteria can be used to facilitate the rep resentation of user preferences in recommender systems as well. Therefore, we can

Multi-Criteria Recommender Systems 5 2.2 Family of Criteria The performance of alternatives in set Items is analyzed upon a set of criteria for each user, in order to model all their characteristics, attributes, effects, or consequences [78, 98]. In recommender systems, the criteria may refer to the multiple features of an item (often the case in content-based recommendations) or to the multiple dimensions upon which the item is being evaluated/rated. Any criterion c can be represented by function gc(i) that expresses the preferences of one user (therefore is user-specific), in order for the user to be able to decide between two alternatives i1 and i2, i.e., whether gc(i1) > gc(i2), in the case that alternative i1 is preferred to alternative i2, or whether gc(i1) = gc(i2), in the case that the two alternatives are considered equivalent (i.e., perfectly substitutable for the particular user on this criterion). To be able to make rational decisions using multiple criteria, it has to be ensured that the whole set of these functions creates a consistent family of criteria [78]. A family of criteria is said to be consistent when it has the following three properties: 1. Monotonic: a family of criteria is monotonic only if, for each pair of alternatives i1 and i2, for which gc1 (i1) > gc1 (i2) for one criterion c1 and gc(i1) = gc(i2) for every other criterion c 6= c1, it can be assumed that alternative i1 is preferred to alternative i2. 2. Exhaustive: a family of criteria is exhaustive only if, for each pair of alternatives i1 and i2, for which gc(i1) = gc(i2) upon each criterion c, we can assume that i1 and i2 are equivalent. 3. Non-redundant: a family of criteria is non-redundant only if the removal of any one of the criteria leads to the violation of one of the other two properties. In the remainder of this chapter, unless explicitly specified otherwise, we will assume that we have a consistent family of k criteria, i.e., g1, g2, . . . , gk . The design of a consistent family of criteria for a given recommendation application has been largely ignored in the recommender systems literature and constitutes an interesting and important problem for future research. Four types of criteria are usually found in MCDM [30]: • Measurable, i.e., a criterion that allows its quantified measurement upon some evaluation scale; • Ordinal, i.e., a criterion that defines an ordered set of acceptable values that allow its evaluation using a qualitative or a descriptive scale; • Probabilistic, i.e., a criterion that uses probability distributions to represent uncertainty in its evaluation; • Fuzzy, i.e., a criterion whose evaluation is represented in relation to its possibility to belong in one of the intervals of a qualitative or descriptive evaluation scale. From a broad perspective, a family of criteria can be used to facilitate the representation of user preferences in recommender systems as well. Therefore, we can