27-12-2011, 09:26 PM
Check out the attachment
07-04-2012, 06:24 AM
need of overview of the project and problem definition
03-08-2012, 02:37 PM
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
1Improving Aggregate.pdf (Size: 1.32 MB / Downloads: 161) Abstract Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate recommendations that have substantially higher aggregate diversity across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating datasets and different rating prediction algorithms. Introduction In the current age of information overload, it is becoming increasingly harder to find relevant content. This problem is not only widespread but also alarming [28]. Over the last 10- 15 years, recommender systems technologies have been introduced to help people deal with these vast amounts of information [1], [7], [9], [30], [36], [39], and they have been widely used in research as well as e-commerce applications, such as the ones used by Amazon and Netflix. The most common formulation of the recommendation problem relies on the notion of ratings, i.e., recommender systems estimate ratings of items (or products) that are yet to be consumed by users, based on the ratings of items already consumed. Recommender systems typically try to predict the ratings of unknown items for each user, often using other users’ ratings, and recommend top N items with the highest predicted ratings. Accordingly, there have been many studies on developing new algorithms that can improve the predictive accuracy of recommendations. Related Work Recommendation Techniques for Rating Prediction Recommender systems are usually classified into three categories based on their approach to recommendation: contentbased, collaborative, and hybrid approaches [1], [3]. Contentbased recommender systems recommend items similar to the ones the user preferred in the past. Collaborative filtering (CF) recommender systems recommend items that users with similar preferences (i.e., “neighbors”) have liked in the past. Finally, hybrid approaches can combine content-based and collaborative methods in several different ways. Recommender systems can also be classified based on the nature of their algorithmic technique into heuristic (or memory-based) and modelbased approaches [1], [9]. Heuristic techniques typically calculate recommendations based directly on the previous user activities (e.g., transactional data or rating values). One of the commonly used heuristic techniques is a neighborhood-based approach that finds nearest neighbors that have tastes similar to those of the target user [9], [13], [34], [36], [40]. In contrast, model-based techniques use previous user activities to first learn a predictive model, typically using some statistical or machine-learning methods, which is then used to make recommendations. Diversity of Recommendations As mentioned in Section 1, the diversity of recommendations can be measured in two ways: individual and aggregate. Most of recent studies have focused on increasing the individual diversity, which can be calculated from each user’s recommendation list (e.g., an average dissimilarity between all pairs of items recommended to a given user) [8], [33], [46], [54], [57]. These techniques aim to avoid providing too similar recommendations for the same user. For example, some studies [8], [46], [57] used an intra-list similarity metric to determine the individual diversity. Alternatively, [54] used a new evaluation metric, item novelty, to measure the amount of additional diversity that one item brings to a list of recommendations. Moreover, the loss of accuracy, resulting from the increase in diversity, is controlled by changing the granularity of the underlying similarity metrics in the diversity-conscious algorithms [33]. Standard Ranking Approach Typical recommender systems predict unknown ratings based on known ratings, using any traditional recommendation technique such as neighborhood-based or matrix factorization CF techniques, discussed in Section 2.1. Then, the predicted ratings are used to support the user’s decision-making. In particular, each user u gets recommended a list of top-N items, LN(u), selected according to some ranking criterion. More formally, item ix is ranked ahead of item iy (i.e., ix |
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