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One-class collaborative filtering (OCCF) problems are ubiquitous in real-world recommendation systems, such as news recommendation, but suffer from data sparsity and lack of negative items. To address the challenge, the state-of-the-art algorithm assigns uninteracted items with smaller weights of being negative and performs low-rank approximation over the user-item interaction matrix. However, the prior ratings are usually suggested to be zero but may not be well-defined. To avert the direct utilization of prior ratings for uninteracted items, we propose a novel ranking-based implicit regularizer by hypothesizing that users’ preference scores for uninteracted items should not deviate a lot from each other. The regularizer is then used in a ranking-based OCCF framework to penalize large differences of preference scores between uninteracted items. To efficiently optimize model parameters in this framework, we develop the scalable alternating least square algorithm and coordinate descent algorithm, whose time complexity is linearly proportional to the data size. Finally, we extensively evaluate the proposed algorithms on six public real-world datasets. The results show that the proposed regularizer significantly improves the recommendation quality of ranking-based OCCF algorithms, such as BPRMF and RankALS. Moreover, the ranking-based framework with the proposed regularizer outperforms the state-of-the-art recommendation algorithms for implicit feedback. [ABSTRACT FROM AUTHOR]

Multi-criteria recommender systems have been increasingly useful for helping consumers identify the most relevant items based on different dimensions of user experiences and highlighting their most valued features. Therefore, researchers have proposed various multi-criteria models to improve recommendation performance. However, most of the existing methods utilize only multi-criteria ratings explicitly provided by the users. Note that explicit multi-criteria ratings are sparse and have the problem of missing values. User reviews, on the other hand, contain richer information of user experiences and reveal multi-dimensional user preferences. Therefore, we propose to use latent multi-criteria ratings generated from user reviews, as opposed to explicit multi-criteria ratings, to provide recommendations and capture latent complex heterogeneous user preferences. Specifically, we propose two novel models for the latent multi-criteria rating generation process: the one-stage model LatentMC-1S that utilizes document hashing method to directly compute latent ratings and the two-stage model LatentMC-2S that uses GRU and Gumbel-Softmax for indirect rating generation. Extensive experiments show that the proposed latent multi-criteria rating approaches outperform explicit ratings across different datasets and performance measures. We also show that latent multi-criteria ratings could be used for imputing missing explicit multi-criteria ratings and thus further improving multi-criteria recommender systems. [ABSTRACT FROM AUTHOR]

The implicit feedback based collaborative filtering (CF) has attracted much attention in recent years, mainly because users implicitly express their preferences in many real-world scenarios. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proved to be helpful to exploit binary relevance data, but lead to either not address the ranking problem, or not specifically focus on top- $k$ k recommendation. Although there exists the listwise method maximizes the Mean Reciprocal Rank (MRR), it has low efficiency and is not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely Collaborative List-and-Pairwise Filtering (CLAPF), which aims to introduce pairwise thinking into listwise methods. Specifically, we smooth another well-known rank-biased measure called Mean Average Precision (MAP), and respectively combine two rank-biased metrics (MAP, MRR) with the pairwise objective function to capture the performance of top- $k$ k recommendation. Furthermore, the sampling scheme for CLAPF is discussed to accelerate the convergence speed. Our CLAPF framework is a new hybrid model that provides an idea of utilizing rank-biased measures in a pairwise way on implicit feedback. Empirical studies demonstrated CLAPF outperforms state-of-the-art approaches on real-world datasets. [ABSTRACT FROM AUTHOR]