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Many bipartite networks describe systems where an edge represents a relation between a user and an item. Measuring the similarity between either users or items is the basis of memory-based collaborative filtering, a widely used method to build a recommender system with the purpose of proposing items to users. When the edges of the network are unweighted, the popular common neighbors-based approaches, allowing only positive similarity values, neglect the possibility and the effect of two users (or two items) being very dissimilar. Moreover, they underperform with respect to model-based (machine learning) approaches, although providing a higher interpretability. Inspired by the functioning of Decision Trees, we propose a method to compute similarity that allows also negative values, the Sapling Similarity. The key idea is to look at how the information that a user is connected to an item influences our prior estimation of the probability that another user is connected to the same item: if it is reduced, then the similarity between the two users will be negative, otherwise it will be positive. We show that, when used to build memory-based collaborative filtering, Sapling Similarity provides better recommendations than existing similarity metrics. Then we compare the Sapling Similarity Collaborative Filtering (SSCF, an hybrid of the item-based and the user-based) with state-of-the-art models using standard datasets. Even if SSCF depends on only one straightforward hyperparameter, it has comparable or higher recommending accuracy, and outperforms all other models on the Amazon-Book dataset, while retaining the high explainability of memory-based approaches. [ABSTRACT FROM AUTHOR]

Recommender systems typically store personal preference profiles. Many items in the profiles can be represented by numerical attributes. However, the initial profile of each user is incomplete and imprecise. One important problem in the development of these systems is how to learn user preferences, and how to automatically adapted update the profiles. To address this issue, this paper presents an unsupervised approach for learning user preferences over numeric attributes by analyzing the interactions between users and recommender systems. When a list of recommendations shown to a target user, the favorite item will be selected by him/her, then the selected item and the over-ranked items will be employed as valuable feedback to learn the user profile. Specifically, two contributions are offered: 1), a learning approach to measure the influence of over-ranked items through analysis of user feedbacks and 2), a weighting algorithm to calculate weights of different attributes by analyzing user selections. These two approaches are integrated into a traditional adaption model for updating user preference profile. Extensive simulations and results show that both approaches are more effective than existing approaches. [ABSTRACT FROM AUTHOR]

Many of our daily life decisions rely on demographic data, which is a good indicator for closeness of people. However, the lack of these data for many online systems let them search for explicit or implicit alternatives. Among many, collaborative filtering is the alternative solutions especially for e-commerce applications where many users are reluctant to disclose their demographic data. This paper explores, discusses and examines many user-profiling approaches for demographic recommender systems (DRSs). These approaches span many alternatives for profiling users in terms of the attribute types, attribute representations, and the profiling way. We present layout, description, and appropriate similarity computation methods for each one of them. A detailed comparison between these different approaches is given using many experiments conducted on a real dataset. The pros and cons of each approach are illustrated for more advantage that may open a window for future work. [ABSTRACT FROM AUTHOR]

Recommender Systems (RSs) are powerful and popular tools for e-commerce. To build their recommendations, RSs make use of varied data sources, which capture the characteristics of items, users, and their transactions. Despite recent advances in RS, the cold start problem is still a relevant issue that deserves further attention, and arises due to the lack of prior information about new users and new items. To minimize system degradation, a hybrid approach is presented that combines collaborative filtering recommendations with demographic information. The approach is based on an existing algorithm, SCOAL (Simultaneous Co-Clustering and Learning), and provides a hybrid recommendation approach that can address the (pure) cold start problem, where no collaborative information (ratings) is available for new users. Better predictions are produced from this relaxation of assumptions to replace the lack of information for the new user. Experiments using real-world datasets show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Nowadays, Knowledge Distillation (KD) has been widely studied for recommender system. KD is a model-independent strategy that generates a small but powerful student model by transferring knowledge from a pre-trained large teacher model. Recent work has shown that the knowledge from the teacher's representation space significantly improves the student model. The state-of-the-art method, named Distillation Experts (DE), adopts cluster-wise distillation that transfers the knowledge of each representation cluster separately to distill the various preference knowledge in a balanced manner. However, it is challenging to apply DE to a new environment since its performance is highly dependent on several key assumptions and hyperparameters that need to be tuned for each dataset and each base model. In this work, we propose a novel method, dubbed Personalized Hint Regression (PHR), distilling the preference knowledge in a balanced way without relying on any assumption on the representation space nor any method-specific hyperparameters. To circumvent the clustering, PHR employs personalization network that enables a personalized distillation to the student space for each user/item representation, which can be viewed as a generalization of DE. Extensive experiments conducted on real-world datasets show that PHR achieves comparable or even better performance to DE tuned by a grid search for all of its hyperparameters. [ABSTRACT FROM AUTHOR]

Review-based recommendation algorithms can alleviate the data sparsity issue in collaborative filtering by combining user ratings and reviews in model learning. However, most existing methods simplify the feature extraction process from reviews by assuming that different granularities of information (e.g., word, review, and feature) are equally important, which cannot optimally leverage the most important information and thus achieves suboptimal recommendation accuracy. Besides, many existing works directly regard text features as users or items representations, which may not be enough to make precise representations due to the large amount of redundant information in reviews. To tackle the two problems mentioned above, we propose a deep learning-based method named H ierarchical A ttention N etwork Oriented Towards C rowd I ntelligence (HANCI). First, HANCI replaces the commonly-used topic models or CNN text processor with an RNN text processor in review feature extraction, which can fully exploit the advantages of the sequential dependencies of reviews by using the whole hidden layers of the bidirectional LSTM as outputs. Second, HANCI weighs the importance of features guided by crowd intelligence to more accurately represent each user on each item, and vice versa. Third, HANCI utilizes a hierarchical attention network based on multi-level review text analysis to extract more precise user preferences and item latent features, so that HANCI can explore the importance of words, the usefulness of reviews and the importance of features to achieve more accurate recommendation. Extensive experiments on three public datasets show that HANCI outperforms the state-of-the-art review-based recommendation algorithms in accuracy and meanwhile provides insightful explanations. [ABSTRACT FROM AUTHOR]