11 ous machine learning techniques[4]. More details on these techniques are discussed Multi-attribute content search and filtering. In some systems, users can explic itly provide their general preferences on multi-attribute content of items that can be used by various searching and filtering techniques to find the most relevant items For example, in [83] users can identify the movie genre, MPAA rating, and film length that they like and specify which attribute is the most important for their de- cision in choosing the movies at the current time. Then the recommender system narrows down the possible choices by searching for the items that match these ad- ditional explicit user preferences. For example, if a user indicates that she wants watch"comedy movies and the movie genre is the most important attribute for her, she will be recommended only comedy movies. Similarly, in [45], users also can provide to the recommender system both the preferred specifications for different content attributes as well as the corresponding importance weights for the different attributes Some of knowledge-based recommender systems [35, 37] can also be classified into this category, because users can provide their general preferences by building their own hierarchical taxonomy tree (i.e, where all item features are modeled in a hierarchical manner) and assigning the relative importance level to each com- ponent in the tree. As a result, the systems recommend the most relevant items according to users' preferences upon the user-defined multiple attributes of item taxonomy. Furthermore, some of hybrid recommender systems with knowledge- based approach would also fit in this category, particularly case-based reasoning recommender systems, where items are represented with multi-criteria content in a structured way (i.e, using a well-defined set of features and feature values)[891 These systems allow users to specify their preferences on multi-attribute content of items in their search for items of interest. For example, several case-based travel recommender systems [74, 76] filter out unwanted items based on each user's pref- erences on multi-attribute content(e.g, locations, services, and activities, and find personalized travel plans for each user by ranking possible travel plans based on the user's preferences and past travel plans of this or similar users. In addition, some case-based recommender systems [9, 71] allow users to"critique"the recommen dation results by refining their requirements as part of the interactive and iterative recommendation process, which uses various search and filtering techniques to con- tinuously provide the user with the updated set of recommendations. For example, when searching for a desktop PC, users can critique the current set of provided rec ommendations by expressing their refined preferences on individual features (e.g cheaper price)or multiple features together(e.g, higher processor speed, RAM, and hard-disk capacity) Multi-criteria rating-based preference elicitation. This category of recommender systems engage multi-criteria ratings, often by extending traditional collaborative filtering approaches, that show users' subjective preferences for various components of individual items. For instance, such systems allow users to rate not only the over all satisfaction from a particular movie, but also the satisfaction from the various
Multi-Criteria Recommender Systems 11 ous machine learning techniques [4]. More details on these techniques are discussed in other chapters ??. Multi-attribute content search and filtering. In some systems, users can explicitly provide their general preferences on multi-attribute content of items that can be used by various searching and filtering techniques to find the most relevant items. For example, in [83] users can identify the movie genre, MPAA rating, and film length that they like and specify which attribute is the most important for their decision in choosing the movies at the current time. Then the recommender system narrows down the possible choices by searching for the items that match these additional explicit user preferences. For example, if a user indicates that she wants to watch “comedy” movies and the movie genre is the most important attribute for her, she will be recommended only comedy movies. Similarly, in [45], users also can provide to the recommender system both the preferred specifications for different content attributes as well as the corresponding importance weights for the different attributes. Some of knowledge-based recommender systems [35, 37] can also be classified into this category, because users can provide their general preferences by building their own hierarchical taxonomy tree (i.e., where all item features are modeled in a hierarchical manner) and assigning the relative importance level to each component in the tree. As a result, the systems recommend the most relevant items according to users’ preferences upon the user-defined multiple attributes of item taxonomy. Furthermore, some of hybrid recommender systems with knowledgebased approach would also fit in this category, particularly case-based reasoning recommender systems, where items are represented with multi-criteria content in a structured way (i.e., using a well-defined set of features and feature values) [89]. These systems allow users to specify their preferences on multi-attribute content of items in their search for items of interest. For example, several case-based travel recommender systems [74, 76] filter out unwanted items based on each user’s preferences on multi-attribute content (e.g., locations, services, and activities), and find personalized travel plans for each user by ranking possible travel plans based on the user’s preferences and past travel plans of this or similar users. In addition, some case-based recommender systems [9, 71] allow users to “critique” the recommendation results by refining their requirements as part of the interactive and iterative recommendation process, which uses various search and filtering techniques to continuously provide the user with the updated set of recommendations. For example, when searching for a desktop PC, users can critique the current set of provided recommendations by expressing their refined preferences on individual features (e.g., cheaper price) or multiple features together (e.g., higher processor speed, RAM, and hard-disk capacity). Multi-criteria rating-based preference elicitation. This category of recommender systems engage multi-criteria ratings, often by extending traditional collaborative filtering approaches, that show users’ subjective preferences for various components of individual items. For instance, such systems allow users to rate not only the overall satisfaction from a particular movie, but also the satisfaction from the various
Gediminas Adomavicius, Nikos Manouselis, YoungOk Kwon movie components( factors), such as the visual effects, the story, or the acting. They differ from the above-surveyed systems in that the users do not indicate their prefer ence or importance weight on the visual effects component for movies in general or to be used in a particular user query, but rather how much they liked the visual effects of the particular movie. One example of such system is the Intelligent Travel Rec- ommender system [74], where users can rate multiple travel items within a"travel bag"(e.g, location, accommodation, etc. ) as well as the entire travel bag. Then, candidate travel plans are ranked according to these user ratings, and the system finds the best match between recommended travel plans and the current needs of a user. These and similar types of multi-criteria rating-based systems are the focus of this chapter and more exemplar systems and techniques are provided in the later In summary, as seen above, many recommender systems that employ traditional content-based, knowledge-based, and hybrid techniques can be viewed as multi- criteria recommender systems, since they model user preferences based on multi- attribute content of items that users preferred in the past or allow users to spec- ify their content-related preferences-i.e, search or filtering conditions for multi- attribute content of items(e.g, identifying the preferred movie genre or providing preferences on multiple pre-defined genre values). However, as mentioned earlier, there is a recent trend in multi-criteria recommendation that studies innovative ap- proaches in collaborative recommendation by engaging multi-criteria ratings. We believe that this additional information on users' preferences offers many opportu- nities for providing novel recommendation support, creating a unique multi-criteria rating environment that has not been extensively researched. Therefore, in the fol owing sections, we survey the state-of-the-art techniques on this particular type of systems that use individual ratings along multiple criteria, which we will refer to as multi-criteria rating recommenders 4 Multi-Criteria Rating Recommendation In this section, we define the multi-criteria rating recommendation problem by for mally extending it from its single-rating counterpart, and provide some further dis- cussion about the advantages that additional criteria may provide in recommender syStems 4.1 Traditional single-rating recommendation problem Traditionally recommender systems operate in a two-dimensional space of Users and Items. The utility of items to users is generally represented by a totally ordered set Ro(e.g, non-negative integers or real numbers within a certain range), and rec-
12 Gediminas Adomavicius, Nikos Manouselis, YoungOk Kwon movie components (factors), such as the visual effects, the story, or the acting. They differ from the above-surveyed systems in that the users do not indicate their preference or importance weight on the visual effects component for movies in general or to be used in a particular user query, but rather how much they liked the visual effects of the particular movie. One example of such system is the Intelligent Travel Recommender system [74], where users can rate multiple travel items within a “travel bag” (e.g., location, accommodation, etc.) as well as the entire travel bag. Then, candidate travel plans are ranked according to these user ratings, and the system finds the best match between recommended travel plans and the current needs of a user. These and similar types of multi-criteria rating-based systems are the focus of this chapter and more exemplar systems and techniques are provided in the later sections. In summary, as seen above, many recommender systems that employ traditional content-based, knowledge-based, and hybrid techniques can be viewed as multicriteria recommender systems, since they model user preferences based on multiattribute content of items that users preferred in the past or allow users to specify their content-related preferences – i.e., search or filtering conditions for multiattribute content of items (e.g., identifying the preferred movie genre or providing preferences on multiple pre-defined genre values). However, as mentioned earlier, there is a recent trend in multi-criteria recommendation that studies innovative approaches in collaborative recommendation by engaging multi-criteria ratings. We believe that this additional information on users’ preferences offers many opportunities for providing novel recommendation support, creating a unique multi-criteria rating environment that has not been extensively researched. Therefore, in the following sections, we survey the state-of-the-art techniques on this particular type of systems that use individual ratings along multiple criteria, which we will refer to as multi-criteria rating recommenders. 4 Multi-Criteria Rating Recommendation In this section, we define the multi-criteria rating recommendation problem by formally extending it from its single-rating counterpart, and provide some further discussion about the advantages that additional criteria may provide in recommender systems. 4.1 Traditional single-rating recommendation problem Traditionally recommender systems operate in a two-dimensional space of Users and Items. The utility of items to users is generally represented by a totally ordered set R0 (e.g., non-negative integers or real numbers within a certain range), and rec-