One of the most efficient methods in collaborative filtering is matrix factorization, which finds the latent vector representations of users and items based on the ratings of users to items. However, a matrix factorization based algorithm suffers from the cold-start problem: it cannot find latent vectors for items to which previous ratings are not available. This paper utilizes click data, which can be collected in abundance, to address the cold-start problem. We propose a probabilistic item embedding model that learns item representations from click data, and a model named EMB-MF, that connects it with a probabilistic matrix factorization for rating prediction. The experiments on three real-world datasets demonstrate that the proposed model is not only effective in recommending items with no previous ratings, but also outperforms competing methods, especially when the data is very sparse.
Wang et. al. @cite_8 proposed Expectation-Maximization Collaborative Filtering (EMCF) which exploits both implicit and explicit feedback for recommendation. For predicting ratings for an item, which does not have any previous ratings, the ratings are inferred from the ratings of its neighbors according to click data.
@cite_8
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Collaborative Filtering (CF) is a popular strategy for recommender systems, which infers users' preferences typically using either explicit feedback (e.g., ratings) or implicit feedback (e.g., clicks). Explicit feedback is more accurate, but the quantity is not sufficient; whereas implicit feedback has an abundant quantity, but can be fairly inaccurate. In this paper, we propose a novel method, Expectation-Maximization Collaborative Filtering (EMCF), based on matrix factorization. The contributions of this paper include: first, we combine explicit and implicit feedback together in EMCF to infer users' preferences by learning latent factor vectors from matrix factorization; second, we observe four different cases of implicit feedback in terms of the distribution of latent factor vectors, and then propose different methods to estimate implicit feedback for different cases in EMCF; third, we develop an algorithm for EMCF to iteratively propagate the estimations of implicit feedback and update the latent factor vectors in order to fully utilize implicit feedback. We designed experiments to compare EMCF with other CF methods. The experimental results show that EMCF outperforms other methods by combining explicit and implicit feedback. One-Class Collaborative Filtering (OCCF) has recently received much attention in recommendation communities due to their close relationship with real industry problem settings. However, the problem with previous research studies on OCCF is that they focused on either rating prediction or ranking prediction, but no concerted research effort has been devoted to developing a recommendation approach that simultaneously optimizes both the ratings and rank of the recommended items. In order to overcome the defects of prior research, a new better unified OCCF approach (UOCCF) based on the newest Collaborative Less-is-More Filtering (CLiMF) approach and the Probabilistic Matrix Factorization (PMF) approach was proposed, which benefits from the ranking-oriented perspective and the rating-oriented perspective by sharing common latent features of users and items in CLiMF and PMF. We also provide an efficient learning algorithm to solve the optimization problem for UOCCF. Experimental results on practical datasets showed that our proposed UOCCF approach outperformed existing OCCF approaches (both ranking-oriented and rating-oriented) over different evaluation metrics, and that the UOCCF approach enjoys the advantage of low complexity and is shown to be linear with the number of observed ratings in a given useritem rating matrix. Because of its high precision and good expansibility, UOCCF is suitable for processing big data, and has wide application prospects in the field of internet information recommendation. Focusing on the problems of over-specialization and concentration bias, this paper presents a novel probabilistic method for recommending items in the neighborhood-based collaborative filtering framework. For the probabilistic neighborhood selection phase, we use an efficient method for weighted sampling of k neighbors that takes into consideration the similarity levels between the target user (or item) and the candidate neighbors. We conduct an empirical study showing that the proposed method increases the coverage, dispersion, and diversity reinforcement of recommendations by selecting diverse sets of representative neighbors. We also demonstrate that the proposed approach outperforms popular methods in terms of item prediction accuracy, utility-based ranking, and other popular measures, across various experimental settings. This performance improvement is in accordance with ensemble learning theory and the phenomenon of "hubness" in recommender systems. The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past user-item relationships. In this work, we propose novel algorithms for predicting user ratings of items by integrating complementary models that focus on patterns at different scales. At a local scale, we use a neighborhood-based technique that infers ratings from observed ratings by similar users or of similar items. Unlike previous local approaches, our method is based on a formal model that accounts for interactions within the neighborhood, leading to improved estimation quality. At a higher, regional, scale, we use SVD-like matrix factorization for recovering the major structural patterns in the user-item rating matrix. Unlike previous approaches that require imputations in order to fill in the unknown matrix entries, our new iterative algorithm avoids imputation. Because the models involve estimation of millions, or even billions, of parameters, shrinkage of estimated values to account for sampling variability proves crucial to prevent overfitting. Both the local and the regional approaches, and in particular their combination through a unifying model, compare favorably with other approaches and deliver substantially better results than the commercial Netflix Cinematch recommender system on a large publicly available data set.