Recommender systems aim to capture user preferences and provide accurate recommendations to users accordingly. A collection of users may have similar preferences with each other, thus forms a community. Since such communities may not be explicitly given, they are formally defined and named Implicit Preference Communities (IPCs) in this paper. By enriching user preferences with the information of other users in the communities, the performance of recommender systems can also be enhanced. In this paper, we propose a recommendation model with Implicit Preference Communities from user ratings and social connections. To tackle the unsupervised learning limitation of IPC modeling, we propose a semi-supervised Bayesian probabilistic graphical model to capture the IPC structure for recommendation. Meanwhile following the spirit of transfer learning, both rating behaviors and social connections are introduced into the model by parameter sharing. Moreover Gibbs sampling based algorithms are proposed for parameter inferences of the models. And to meet the need for online scenarios when data arrives as a stream, a novel online sampling based parameter inference algorithm is proposed. Extensive experiments on seven real-world datasets have been conducted and compared with fourteen state-of-art recommendation algorithms. Statistically significant improvements verify the effectiveness of the proposed IPC-aware recommendation models.
Rank fusion is a powerful technique that allows multiple sources of information to be combined into a single result set. However, to date fusion has not been regarded as being cost-effective in cases where strict per- query efficiency guarantees are required, such as in web search. In this work we propose a novel solution to rank fusion by splitting the computation into two parts ? one phase that is carried out offline to generate pre-computed centroid answers for queries with broadly similar information needs, and then a second online phase that uses the corresponding topic centroid to compute a result page for each query. We explore efficiency improvements to classic fusion algorithms whose costs can be amortized as a pre-processing step, and can then be combined with re-ranking approaches to dramatically improve effectiveness in multi-stage retrieval systems with little efficiency overhead at query time. Experimental results using the ClueWeb12B collection and the UQV100 query variations demonstrate that centroid-based approaches allow improved retrieval effectiveness at little or no loss in query throughput or latency, and with reasonable pre-processing requirements. We additionally show that queries that do not match any of the pre-computed clusters can be accurately identified and efficiently processed in our proposed ranking pipeline.
Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
Next and next new Point-of-interest (POI) recommendation are essential instruments in promoting customer experiences and business operations related to locations. However, due to the sparsity of the check-in records, they still remain insufficiently studied. In this paper, we propose to utilize personalized latent behavior patterns learned from contextual features, e.g., time of day, day of week, and location category, to improve the effectiveness of the recommendations. Two variations of models are developed, including GPDM which learns a fixed pattern distribution for all users and PPDM which learns personalized pattern distribution for each user. In both models, a soft-max function is applied to integrate the personalized Markov chain with the latent patterns, and a sequential Bayesian Personalized Ranking (S-BPR) is applied as the optimization criterion. Then, Expectation Maximization (EM) is in charge of finding optimized model parameters. Extensive experiments on three large-scale commonly adopted real-world LBSN datasets prove that the inclusion of location category and latent patterns helps to boost the performance of POI recommendations. Specifically, our models in general significantly outperform other state-of-the-art methods for both next and next new POI recommendation tasks. Moreover, our models are capable of making accurate recommendations no matter they are short/long duration or distance.
Question answering over knowledge base (KB-QA) aims to take full advantage of the knowledge in knowledge bases with the ultimate purpose of returning answers to questions. To access the substantial knowledge within the KB, many model architectures are hindered by the bottleneck of accurately predicting relations which connect subject entities to object entities. To break the bottleneck, this paper presents a novel framework which can be viewed as an extension to APVA-TURBO. Experimental results show a boost in performance to the APVA-TURBO approach and outperform other question answering approaches.
An intensive recent research work investigated the combined use of hand-curated knowledge sources and corpus-driven sources to learn effective text representations. The overall learning process could be run by online revising the learning objective or by offline refining an original learned representation. The differentiated impact of each of the learning approaches on the quality of the learned representations has not been so far studied in the literature. This article focuses on the design of comparable offline vs. online knowledge-enhanced document representation learning models and the comparison of their effectiveness using a set of standard IR and NLP downstream tasks. The results of quantitative and qualitative analyses show that 1) offline vs. online learning approaches have dissimilar results trends regarding the task as well as the dataset distribution counts with regard to domain application; 2) while considering relational semantics is undoubtedly beneficial, the way used to express relational constraints could affect semantic inference effectiveness. The findings of this work present opportunities for the design of future representation learning models, but also for providing insights about the evaluation of such models.