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Human behaviors in recommendation systems are driven by many high-level, complex, and evolving intentions behind their decision making processes. In order to achieve better performance, it is important for recommendation systems to be aware of user intentions besides considering the historical interaction behaviors. However, user intentions are seldom fully or easily observed in practice, so that the existing works are incapable of fully tracking and modeling user intentions, not to mention using them effectively into recommendation. In this paper, we present the Intention-Aware Sequential Recommendation (ISRec) method, for capturing the underlying intentions of each user that may lead to her next consumption behavior and improving recommendation performance. Specifically, we first extract the intentions of the target user from sequential contexts, then take complex intent transition into account through the message-passing mechanism on an intention graph, and finally obtain the future intentions of this target user from inference on the intention graph. The sequential recommendation for a user will be made based on the predicted user intentions, offering more transparent and explainable intermediate results for each recommendation. Extensive experiments on various real-world datasets demonstrate the superiority of our method against several state-of-the-art baselines in sequential recommendation in terms of different metrics. [ABSTRACT FROM AUTHOR]

Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems. However, most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. This issue calls for the need of Large-scale Machine Learning (LML), which aims to learn patterns from big data with comparable performance efficiently. In this paper, we offer a systematic survey on existing LML methods to provide a blueprint for the future developments of this area. We first divide these LML methods according to the ways of improving the scalability: 1) model simplification on computational complexities, 2) optimization approximation on computational efficiency, and 3) computation parallelism on computational capabilities. Then we categorize the methods in each perspective according to their targeted scenarios and introduce representative methods in line with intrinsic strategies. Lastly, we analyze their limitations and discuss potential directions as well as open issues that are promising to address in the future. [ABSTRACT FROM AUTHOR]

Personalized review-based rating prediction, a newly emerged research problem, aims at inferring users’ ratings over their unrated items using existing reviews and corresponding ratings. While some researchers proposed to learn topic factor from review text to obtain interpretability for rating prediction, they often overlooked the fact that the learned topic factors are limited to review text and cannot fully reveal the complicated relations between reviews and ratings. Moreover, topic modeling based solutions for this problem usually utilize Gibbs sampling algorithms to learn topics and word distributions, resulting in non-negligible computational overload. To address the above challenges, we propose an integrated topic and latent factor model (ITLFM), which combines topic and latent factors in a linear way to make them complement each other for better accuracies in rating prediction tasks. In addition, ITLFM models review text through an additive topic model to reveal user's and item's topic factors simultaneously. To ensure high learning efficiency, we design a hybrid stochastic learning algorithm for ITLFM. We evaluate ITLFM on several standard benchmarks and compare with representative approaches. The experimental results demonstrate that the proposed ITLFM method is computationally efficient and accurate, as well as scalable for large scale applications. [ABSTRACT FROM PUBLISHER]

We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques. [ABSTRACT FROM PUBLISHER]