336 ROBIN BURKE New Item: Similarly, a new item that has not had many ratings also cannot be easily recommended: the 'new item'problem. This problem shows up in domains such as news articles where there is a constant stream of new items and each user only rates a few. It is also known as the early rater' problem, since the first person to rate an item gets little benefit from doing so: such early ratings do not improve a user's ability to match against others(Avery Zeckhauser, 1997). This makes it necessary for recommender systems to provide other incentives to encourage users to provide ratings Collaborative recommender systems depend on overlap in ratings across users and have difficulty when the space of ratings is sparse: few users have rated the same tems. The sparsity problem is somewhat reduced in model-based approaches, such as singular value decomposition(Strang, 1988), which can reduce the dimensionality of the space in which comparison takes place(Foltz, 1990; Rosenstein& Lochbaum 2000). Still sparsity is a significant problem in domains such as news filtering, since there are many items available and, unless the user base is very large, the odds that another user will share a large number of rated items is small These three problems suggest that pure collaborative techniques are best suited to oblems where the density of user interest is relatively high across a small and stati universe of items. If the set of items changes too rapidly, old ratings will be of little value to new users who will not be able to have their ratings compared to those of the existing users. If the set of items is large and user interest thinly spread, then the probability of overlap with other users will be small Collaborative recommenders work best for a user who fits into a niche with many neighbors of similar taste. The technique does not work well for so-called 'gray sheep'(Claypool et al., 1999), who fall on a border between existing cliques of users This is also a problem for demographic systems that attempt to categorize users on personal characteristics. On the other hand, demographic recommenders do not have the 'new user'problem, because they do not require a list of ratings from user.Instead they have the problem of gathering the requisite demographic information. With sensitivity to on-line privacy increasing, especially in electronic commerce contexts (USITIC, 1997), demographic recommenders are likely to remain rare: the data most predictive of user preference is likely to be information that users are reluctant to disclose Content-based techniques also have a start-up problem in that they must accumu- late enough ratings to build a reliable classifier. Relative to collaborative filtering, content-based techniques also have the problem that they are limited by the features that are explicitly associated with the objects that they recommend. For example, content-based movie recommendation can only be based on written materials about a movie: actors' names, plot summaries, etc. because the movie itself is opaque to the stem.This puts these techniques at the mercy of the descriptive data available Collaborative systems rely only on user ratings and can be used to recommend items without any descriptive data. Even in the presence of descriptive data, some exper- Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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HYBRID RECOMMENDER SYSTEMS SURVEY AND EXPERIMENTS 337 iments have found that colla borative recommender systems can be more accurate than content-based ones(Alspector et al., 1997) The great power of the collaborative approach relative to content-based ones is its cross-genre or 'outside the box'recommendation ability It may be that listeners who enjoy free jazz also enjoy avant-garde classical music, but a content-based recommender trained on the preferences of a free jazz aficionado would not be able to suggest items in the classical realm since none of the features(performers, instruments, repertoire) associated with items in the different categories would be shared Only by looking outside the preferences of the individual can such sugges- tions be mad Both content-based and collaborative techniques suffer from the'portfolio effect An ideal recommender would not suggest a stock that the user already owns or a movie she has already seen. The problem becomes quite tricky in domains such as news filtering, since stories that look quite similar to those already read may in fact present some new facts or new perspectives that would be valuable to the user.At the same time, many different presentations of the same wire-service story from different newspapers would not be useful. The Daily Learner system (Billsus Pazzani, 2000)uses an upper bound of similarity in its content-based recom mender to filter out news items too similar to those already seen by the use Utility-based and knowledge-based recommenders do not have ramp-up or spar- sity problems, since they do not base their recommendations on accumulated stat istical evidence. Utility-based techniques require that the system build a complete utility function across all features of the objects under consideration. One benefit of this approach is that it can incorporate many different factors that contribute to the value of a product, such as delivery schedule, warranty terms r conceivably the user's existing portfolio, rather than just product-specif features. In addition, these non-product features may have extremely idiosyncratic utility: how soon something can be delivered may matter very much to a user facing a deadline. A utility-based framework thereby lets the user express all of the con- siderations that need to go into a recommendation. For this reason, Guttman(1999) describes Tete-a-Tete as ' product and merchant brokering 'system rather than recommender system. However, under the definition given above, Tete-a-Tete does fit since its main output is a recommendation(a top-ranked item) that is generated on a personalized basis The flexibility of utility-based systems is also to some degree a failing. The user must construct a complete preference function, and must therefore weigh the sig- nificance of each possible feature. Often this creates a significant burden of interaction. Tete-a-Tete uses a small number of stereotype'preference functions to get the user started, but ultimately the user needs to look at, weigh, and select preference function for each fea s an item o be feasible for items with only a few characteristics, such as price, quality and delivery date, but not for more complex and subjective domains like movies or news articles. Persona Logic does not require the user to input a utility function, but Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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ROBIN BURKE instead derives the function through an interactive questionnaire. while the com plete explicit utility function might be a boon to some users, for example, technical Isers with specific purchasing requirements, it is likely to overwhelm a more casual user with a less-detailed knowledge. Large moves in the product space, for example, from'sports carsto 'family cars'require a complete re-tooling of the preference function, including everything from interior space to fuel economy. This makes a utility-based system less appropriate for the casual browser nowledge-based recommender systems are prone to the drawback of al knowledge-based systems: the need for knowledge acquisition. There are three types of knowledge that are involved in such a system Catalog knowledge: Knowledge about the objects being recommended and their features. For example, the Entree recommender should know that 'Thai cuisine is a kind of asian cuis unctional knowledge: The system must be able to map between the user's needs and the object that might satisfy those needs. For example, Entree knows that a need for a romantic dinner spot could be met by a restaurant that is quiet with an ocean vIew User knowledge: To provide good recommendations, the system must have some knowledge about the user. This might take the form of general demographic in formation or specific information about the need for which a recommendation is sought. Of these knowledge types, the last is the most challenging, as it is, in the worst case, an instance of the general user-modeling problem(Towle Quinn, 2000) Despite this drawback, knowledge-based recommendation has some beneficial characteristics. It is appropriate for casual exploration, because it demands less of the user than utility-based recommendation. It does not involve a start-up period during which its suggestions are low quality. a knowledge-based recommender can not'discover'user niches, the way collaborative systems can. On the other hand, it can make recommendations as wide-ranging as its knowledge base allows Table II summarizes the five recommendation techniques that we have discussed here, pointing out the pros and cons of each. Collaborative and demographic tech niques have the unique capacity to identify cross-genre niches and can entice users to jump outside of the familiar, Knowledge-based techniques can do the same but only if such associations have been identified ahead of time by the knowledge All of the learning-based techniques(collaborative, content-based and demo graphic) suffer from the ramp-up problem in one form or another. The converse of this problem is the stability vs. plasticity problem for such learners. Once a user's profile has been established in the system, it is difficult to change one's preferences etarian will cor dations from a content-based or collaborative recommender for some time. until Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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HYBRID RECOMMENDER SYSTEMS, SURVEY AND EXPERIMENTS newer ratings have the chance to tip the scales. Many adaptive systems include some sort of temporal discount to cause older ratings to have less influence, but they do so at the risk of losing information about interests that are long-term but sporadically exercised(Billsus Pazzani, 2000; Schwab et al., 2001). For example, a user might like to read about major earthquakes when they happen, but such occurrences are sufficiently rare that the ratings associated with last year's earthquake are gone by the time the next big one hits. Knowledge- and utility-based recommenders respond to the user's immediate need and do not need any kind of retraining when The ramp-up problem has the side-effect of excluding casual users from receiving the full benefits of colla borative and content-based recommendation. It is possible to do simple market-basket recommendation with minimal user input Amazon.com's'peoplewhoboughtXalsoboughtYbutthismechanismhas few of the advantages commonly associated with the collaborative filtering concept The learning-based technologies work best for dedicated users who are willing to invest some time making their preferences known to the system. Utility-and knowledge-based systems have fewer problems in this regard because they do not rely on having historical data about a user's preferences. Utility-based systems may present difficulties for casual users who might be unwilling to tailor a utility function simply to browse a catalog 3. Hybrid recommender systems Hybrid recommender systems combine two or more recommendation techniques to gain better performance with fewer of the drawbacks of any individual one. Most commonly, collaborative filtering is combined with some other technique in an met pt to avoid the ramp-up problem. Table IIT shows some of the combination 3.1. WEIGHTED A weighted hybrid recommender is one in which the score of a recommended item is computed from the results of all of the available recommendation techniques present in the system. For example, the simplest combined hybrid would be a linear com- bination of recommendation scores. The P-Tango system(Claypool et al., 1999) uses such a hybrid. It initially gives collaborative and content-based recommenders equal weight, but gradually adjusts the weighting as predictions about user ratings are confirmed or disconfirmed. Pazzani,s combination hybrid does not use numeric scores, but rather treats the output of each recommender(collaborative, con- tent-based and demographic)as a set of votes, which are then combined in a con sensus scheme(Pazzani, 1999 The benefit of a weighted hybrid is that all of the system's capabilities are brought to bear on the recommendation process in a straightforward way and it is easy to Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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ROBIN BURKE Table Ill. Hybridization methods Hybridization method Description The scores(or votes) of several recommendation techniques are combined together to produce a single recommendation Switching The system switches between recommendation techniques depending the current situation Mixed Recommendations from several different recommenders are presented at the same time Feature combination Features from different recommendation data sources are thrown together into a single recommendation algorithm Cascade One recommender refines the recommendations given by another. Feature augmentation Output from one technique is used as an input feature to another. Meta-level The model learned by one recommender is used as input to another perform post-hoc credit assignment and adjust the hybrid accordingly. However, the implicit assumption in this technique is that the relative value of the different tech- niques is more or less uniform across the space of possible items From the discussion above, we know that this is not always so: a collaborative recommender will be weaker for those items with a small number of raters 3.2. SWITCHING A switching hybrid builds in item-level sensitivity to the hy bridization strategy: the stem uses some criterion to switch between recommendation techniques. The Daily Learner system uses a content/ collaborative hybrid in which a content-based recommendation method is employed first. If the content-based system cannot make a recommendation with sufficient confidence. then a collabora tive recommendation is attempted. This switching hybrid does not completely avoid the ramp-up pro- blem, since both the collaborative and the content-based systems have the ' new user problem. However, Daily Learner's content-based technique is nearest-neighbor which does not require a large number of examples for accurate classification What the collaborative technique provides in a switching hybrid is the ability to cross genres, to come up with recommendations that are not close in a semantic way to the items previous rated highly, but are still relevant. For example, in the case of Daily Learner, a user who is interested in the Microsoft anti-trust trial might also be interested in the aol/Time Warner merger. Content matching would not be likely to recommend the merger stories, but other users with an interest in corporate power in the high-tech industry may be rating both sets of stories highly, enabling the system to make the recommendation colla boratively Daily Learner's hybrid has a 'fallback'character- the short-term model is always used first and the other technique only comes into play when that technique fails Tran and Cohen(1999)proposed a more straightforward switching hybrid. In their and one long-term, and the fallback strategy is short-term/collaborative/long-ter e short-term 3 Actually Billsus' system has two content-based recommendation algorithms, or Reproduced with permission of the copyright owner. Further reproduction prohibited without permission
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