Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip and collect. Among various multi-behavior recommendation methods, non-sampling methods have shown superiority over negative sampling methods. However, two observations are usually ignored in existing state-of-the-art non-sampling methods based on binary regression: (1) users have different preference strengths for different items, so they cannot be measured simply by binary implicit data; (2) the dependency across multiple behaviors varies for different users and items. To tackle the above issue, we propose a novel non-sampling learning framework namedCriterion-guidedHeterogeneousCollaborativeFiltering (CHCF). CHCF introduces both upper and lower thresholds to indicate selection criteria, which will guide user preference learning. Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior. We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by the CHCF learning framework in a non-sampling form effectively. Extensive experiments on three real-world datasets show the effectiveness of CHCF in heterogeneous scenarios.

Rapid and accurate prediction of user preferences is the ultimate goal of today’s recommender systems. More and more researchers pay attention to multi-behavior recommender systems which utilize the auxiliary types of user-item interaction data, such as page view and add-to-cart to help estimate user preferences. Recently, graph-based methods were proposed to showcase an advanced capability in representation learning and capturing collaborative signals. However, we argue that these methods ignore the intrinsic difference between the two types of nodes in the bipartite graph and aggregate information from neighboring nodes with the same functions. Besides, these models do not fully explore the collaborative signals implied by the meta-path across different types of behavior, which causes a huge loss of the potential semantic information across behaviors. To address the above limitations, we present a unified graph model named SaGCN (short for Semantic-aware Graph Convolutional Networks). Specifically, we construct separate user-user and item-item graphs by meta-path, and apply separate aggregation and transformation functions to propagate user and item information. To perform better semantic propagation, we design a relation composition function and a semantic propagation architecture for heterogeneous collaborative filtering signals learning. Extensive experiments on two real-world datasets show that SaGCN outperforms a wide range of state-of-the-art methods in multi-behavior scenarios.

In this paper, we show that most multiple term queries include more than one topic and users usually reformulate their queries by topics instead of terms. In order to provide empirical evidence on user's reformulation behavior and to help search engines better handle the query reformulation problem, we focus on detecting internal topics in the original query and analyzing users. reformulation to those topics. Particularly, we utilize the Interaction Information (II) to measure the degree of one sub-query being a topic based on the local search results. The experimental results on query log show that: most users reformulate query at the topical level; and our proposed II-based algorithm is a good method to detect topics from original queries.

Finding informative genes from microarray data is an important research problem in bioinformatics research and applications. Most of the existing methods rank features according to their discriminative capability and then find a subset of discriminative genes (usually top k genes). In particular, t-statistic criterion and its variants have been adopted extensively. This kind of methods rely on the statistics principle of t-test, which requires that the data follows a normal distribution. However, according to our investigation, the normality condition often cannot be met in real data sets.To avoid the assumption of the normality condition, in this paper, we propose a rank sum test method for informative gene discovery. The method uses a rank-sum statistic as the ranking criterion. Moreover, we propose using the significance level threshold, instead of the number of informative genes, as the parameter. The significance level threshold as a parameter carries the quality specification in statistics. We follow the Pitman efficiency theory to show that the rank sum method is more accurate and more robust than the t-statistic method in theory.To verify the effectiveness of the rank sum method, we use support vector machine (SVM) to construct classifiers based on the identified informative genes on two well known data sets, namely colon data and leukemia data. The prediction accuracy reaches 96.2% on the colon data and 100% on the leukemia data. The results are clearly better than those from the previous feature ranking methods. By experiments, we also verify that using significance level threshold is more effective than directly specifying an arbitrary k.

Organizing Web search results into clusters facilitates users' quick browsing through search results. Traditional clustering techniques are inadequate since they don't generate clusters with highly readable names. In this paper, we reformalize the clustering problem as a salient phrase ranking problem. Given a query and the ranked list of documents (typically a list of titles and snippets) returned by a certain Web search engine, our method first extracts and ranks salient phrases as candidate cluster names, based on a regression model learned from human labeled training data. The documents are assigned to relevant salient phrases to form candidate clusters, and the final clusters are generated by merging these candidate clusters. Experimental results verify our method's feasibility and effectiveness.