Understanding user preference is essential to the optimization of recommender systems. As a feedback of user's taste, rating scores can directly reflect the preference of a given user to a given product. Uncovering the latent components of user ratings is thus of significant importance for learning user interests. In this paper, a new recommendation approach, called LCR, was proposed by investigating the latent components of user ratings. The basic idea is to decompose an existing rating into several components via a cost-sensitive learning strategy. Specifically, each rating is assigned to several latent factor models and each model is updated according to its predictive errors. Afterwards, these accumulated predictive errors of models are utilized to decompose a rating into several components, each of which is treated as an independent part to retrain the latent factor models. Finally, all latent factor models are combined linearly to estimate predictive ratings for users. In contrast to existing methods, LCR provides an intuitive preference modeling strategy via multiple component analysis at an individual perspective. Meanwhile, it is verified by the experimental results on several benchmark datasets that the proposed method is superior to the state-of-art methods in terms of recommendation accuracy.
Comparing to single-criteria recommender systems, multi-criteria recommender system contains more information, including ratings of item attributes. The complexity of algorithms is increased by the additional information, but in most cases, the quality of recommendation can also be improved by incorporating the auxiliary data @cite_31 @cite_45 @cite_16 @cite_11 . To the best of our knowledge, examples of multi-criteria recommender systems include Zagat's Guide, Buy.com and Yahoo! Movies @cite_38 . To exploit the information of multi-criteria ratings, a commonly used method is to extend the computation of similarity, from single-criteria ratings to multi-criteria ratings @cite_45 @cite_16 . For example, within a user-based collaborative filtering approach, the similarity of two users is computed by making use of their single-criteria ratings while in a multi-criteria recommender system, the computation of similarity is performed on each criteria and finally average the result over all criteria. Lee @cite_49 extended the concept of single criteria rating to multi-criteria ones and utilized skyline queriy algorithm to find candidate items. Jannach @cite_45 made use of support vector regression to determine the relative importance of multi-criteria ratings and combined user-based and item-based regression model in a weighted way.
@cite_38 @cite_45 @cite_49 @cite_31 @cite_16 @cite_11
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Many websites provide visitors with the possibility to evaluate each item on more than one criteria. A commonly used rating scale is the one to five-star rating system or similar linguistic scales. Such scales are ordinal but the symbolic or lexical semantics convey information about the strength of user references in addition to the order of rated items. We refer to such scales as discrete ordered scales. We present AHP-Rec a method that treats user ratings as interval scale data and uses a multi-criteria approach for deriving predictions for user ratings. We use the data provided by Yahoo! Movies to demonstrate and evaluate the AHP-Rec recommender method. AHP-Rec takes as input the ratings each user gives to movies, calculates weights for each scale item that are personal for each user and provides its recommendation by aggregating preferences of similar users. Our method provides improved results over the state of the art single criterion method SVD++ and the multi-criteria method UTARec. Recommender systems (RS) have shown to be valuable tools on e-commerce sites which help the customers identify the most relevant items within large product catalogs. In systems that rely on collaborative filtering, the generation of the product recommendations is based on ratings provided by the user community. While in many domains users are only allowed to attach an overall rating to the items, increasingly more online platforms allow their customers to evaluate the available items along different dimensions. Previous work has shown that these criteria ratings contain valuable information that can be exploited in the recommendation process. In this work we present new methods to leverage information derived from multi-dimensional ratings to improve the predictive accuracy of such multi-criteria recommender systems. In particular, we propose to use Support Vector regression to determine the relative importance of the individual criteria ratings and suggest to combine user- and item-based regression models in a weighted approach. Beside the automatic adjustment and optimization of the combination weights, we also explore different feature selection strategies to further improve the quality of the recommendations. An experimental analysis on two real-world rating datasets reveals that our method outperforms both recent single-rating algorithms based on matrix factorization as well as previous methods based on multi-criteria ratings in terms of the predictive accuracy. We therefore see the usage of multi-criteria customer ratings as a promising opportunity for e-commerce sites to improve the quality and precision of their online recommendation services. Recommendation systems utilize information techniques to the problem of helping users find the items they would like. Example applications include the recommendation systems for movies, books, CDs and many others. As recommendation systems emerge as an independent research area, the rating structure plays a critical role in recent studies. Among many alternatives, the collaborative filtering algorithms are generally accepted to be successful to estimate user ratings of unseen items and then to derive proper recommendations. In this paper, we extend the concept of single criterion ratings to multi-criteria ones, i.e., an item can be evaluated in many different, aspects. Since there are usually conflicts among different criteria, the recommendation problem cannot be formulated as an optimization problem any more. Instead, we propose to use data query techniques to solve this multi-criteria recommendation problem. Personalization technologies and recommender systems help online consumers avoid information overload by making suggestions regarding which information is most relevant to them. Most online shopping sites and many other applications now use recommender systems. Two new recommendation techniques leverage multicriteria ratings and improve recommendation accuracy as compared with single-rating recommendation approaches. Taking full advantage of multicriteria ratings in personalization applications requires new recommendation techniques. In this article, we propose several new techniques for extending recommendation technologies to incorporate and leverage multicriteria rating information. The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches. Research in recommender systems is now starting to recognise the importance of multiple selection criteria to improve the recommendation output. In this paper, we present a novel approach to multi-criteria recommendation, based on the idea of clustering users in "preference lattices" (partial orders) according to their criteria preferences. We assume that some selection criteria for an item (product or a service) will dominate the overall ranking, and that these dominant criteria will be different for different users. Following this assumption, we cluster users based on their criteria preferences, creating a "preference lattice". The recommendation output for a user is then based on ratings by other users from the same or close clusters. Having introduced the general approach of clustering, we proceed to formulate three alternative recommendation methods instantiating the approach: (a) using the aggregation function of the criteria, (b) using the overall item ratings, and (c) combining clustering with collaborative filtering. We then evaluate the accuracy of the three methods using a set of experiments on a service ranking dataset, and compare them with a conventional collaborative filtering approach extended to cover multiple criteria. The results indicate that our third method, which combines clustering and extended collaborative filtering, produces the highest accuracy.