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Register a new userCollaborative Similarity Embedding for Recommender Systems
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Added by
Chih-Ming Chen
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representation learning and recommendation. In the proposed framework, we differentiate two types of proximity relations: direct proximity and k-th order neighborhood proximity. While learning from the former exploits direct user-item associations observable from the graph, learning from the latter makes use of implicit associations such as user-user similarities and item-item similarities, which can provide valuable information especially when the graph is sparse. Moreover, for improving scalability and flexibility, we propose a sampling technique that is specifically designed to capture the two types of proximity relations. Extensive experiments on eight benchmark datasets show that CSE yields significantly better performance than state-of-the-art recommendation methods.
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Among various recommender techniques, collaborative filtering (CF) is the most successful one. And a key problem in CF is how to represent users and items. Previous works usually represent a user (an item) as a vector of latent factors (aka. textit{embedding}) and then model the interactions between users and items based on the representations. Despite its effectiveness, we argue that its insufficient to yield satisfactory embeddings for collaborative filtering. Inspired by the idea of SVD++ that represents users based on themselves and their interacted items, we propose a general collaborative filtering framework named DNCF, short for Dual-embedding based Neural Collaborative Filtering, to utilize historical interactions to enhance the representation. In addition to learning the primitive embedding for a user (an item), we introduce an additional embedding from the perspective of the interacted items (users) to augment the user (item) representation. Extensive experiments on four publicly datasets demonstrated the effectiveness of our proposed DNCF framework by comparing its performance with several traditional matrix factorization models and other state-of-the-art deep learning based recommender models.
To cope with the ever-growing information overload, an increasing number of digital libraries employ content-based recommender systems. These systems traditionally recommend related documents with the help of similarity measures. However, current document similarity measures simply distinguish between similar and dissimilar documents. This simplification is especially crucial for extensive documents, which cover various facets of a topic and are often found in digital libraries. Still, these similarity measures neglect to what facet the similarity relates. Therefore, the context of the similarity remains ill-defined. In this doctoral thesis, we explore contextual document similarity measures, i.e., methods that determine document similarity as a triple of two documents and the context of their similarity. The context is here a further specification of the similarity. For example, in the scientific domain, research papers can be similar with respect to their background, methodology, or findings. The measurement of similarity in regards to one or more given contexts will enhance recommender systems. Namely, users will be able to explore document collections by formulating queries in terms of documents and their contextual similarities. Thus, our research objective is the development and evaluation of a recommender system based on contextual similarity. The underlying techniques will apply established similarity measures and as well as neural approaches while utilizing semantic features obtained from links between documents and their text.
Personalization collaborative filtering recommender systems (CFRSs) are the crucial components of popular e-commerce services. In practice, CFRSs are also particularly vulnerable to shilling attacks or profile injection attacks due to their openness. The attackers can carefully inject chosen attack profiles into CFRSs in order to bias the recommendation results to their benefits. To reduce this risk, various detection techniques have been proposed to detect such attacks, which use diverse features extracted from user profiles. However, relying on limited features to improve the detection performance is difficult seemingly, since the existing features can not fully characterize the attack profiles and genuine profiles. In this paper, we propose a novel detection method to make recommender systems resistant to the shilling attacks or profile injection attacks. The existing features can be briefly summarized as two aspects including rating behavior based and item distribution based. We firstly formulate the problem as finding a mapping model between rating behavior and item distribution by exploiting the least-squares approximate solution. Based on the trained model, we design a detector by employing a regressor to detect such attacks. Extensive experiments on both the MovieLens-100K and MovieLens-ml-latest-small datasets examine the effectiveness of our proposed detection method. Experimental results were included to validate the outperformance of our approach in comparison with benchmarked method including KNN.
Users of industrial recommender systems are normally suggesteda list of items at one time. Ideally, such list-wise recommendationshould provide diverse and relevant options to the users. However, in practice, list-wise recommendation is implemented as top-N recommendation. Top-N recommendation selects the first N items from candidates to display. The list is generated by a ranking function, which is learned from labeled data to optimize accuracy.However, top-N recommendation may lead to suboptimal, as it focuses on accuracy of each individual item independently and overlooks mutual influence between items. Therefore, we propose a personalized re-ranking model for improving diversity of the recommendation list in real recommender systems. The proposed re-ranking model can be easily deployed as a follow-up component after any existing ranking function. The re-ranking model improves the diversity by employing personalized Determinental Point Process (DPP). DPP has been applied in some recommender systems to improve the diversity and increase the user engagement.However, DPP does not take into account the fact that users may have individual propensities to the diversity. To overcome such limitation, our re-ranking model proposes a personalized DPP to model the trade-off between accuracy and diversity for each individual user. We implement and deploy the personalized DPP model on alarge scale industrial recommender system. Experimental results on both offline and online demonstrate the efficiency of our proposed re-ranking model.
As one of major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, so-called the item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. To our surprise, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely these new items will have more chance to appear in other users recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.
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