Latest Articles

## Geographic Diversification of Recommended POIs in Frequently Visited Areas

In the personalized Point-Of-Interest (POI) (or venue) recommendation, the diversity of recommended POIs is an important aspect. Diversity is... (more)

## Understanding Assimilation-contrast Effects in Online Rating Systems: Modelling, Debiasing, and Applications

“Unbiasedness,” which is an important property to ensure that users’ ratings indeed reflect their true evaluations of... (more)

News recommendation has become an essential way to help readers discover interesting stories. While a growing line of research has focused on modeling reading preferences for news recommendation, they neglect the instability of reader consumption behaviors, i.e., consumption behaviors of readers may be influenced by other factors in addition to... (more)

## Explainable Product Search with a Dynamic Relation Embedding Model

Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on... (more)

### Call for Special Issue Proposals

ACM Transactions on Information Systems (TOIS) invites proposals for a special issue of the journal devoted to any topic in information retrieval. Click here to see more details.

### New options for ACM authors to manage rights and permissions for their work

ACM introduces a new publishing license agreement, an updated copyright transfer agreement, and a new authorpays option which allows for perpetual open access through the ACM Digital Library. For more information, visit the ACM Author Rights webpage.

ACM Transactions on Information Systems (TOIS) is a scholarly journal that publishes previously unpublished high-quality scholarly articles in all areas of information retrieval. TOIS is published quarterly.

##### Forthcoming Articles
Local Variational Feature-based Similarity Models for Recommending Top-N New Items

Top-N recommender systems have been studied extensively. Item-based collaborative filtering recommendation algorithms show promising results for the problem. They predict a user's preferences by estimating similarities between a target and user rated items. Top-$N$ recommendation remains a challenging task in the scenarios where there is a lack of preference history for new items. FSM address this problem by extending item-based CF by estimating similarity functions of item features. The quality of the estimated similarity function determines the accuracy of the recommendation. However, existing FSM only estimate global similarity functions, i.e., estimates using preference information across all users. Moreover, the estimated similarity functions are linear; hence may fail to capture the complex structure underlying item features. In this paper, we propose to improve FSM by estimating local similarity functions, where each function is estimated for a subset of like-minded users. To capture global patterns, we extend the global similarity function from linear to non-linear, based on the effectiveness of VAE. We propose a Bayesian generative model, the LVSM, to encapsulate local and global similarity functions. We present a variational EM algorithm for efficient approximate inference. Extensive experiments on a large number of real-world datasets demonstrate the effectiveness of our proposed model.

Emotional Conversation Generation Based on Bayesian Deep Neural Network

The field of conversation generation using neural networks has attracted increasing attention from researchers for several years. However, traditional neural language models tend to generate a generic reply with poor semantic logic and no emotion. This paper proposes an emotional conversation generation model based on a Bayesian deep neural network that can generate replies with rich emotions, clear themes, and diverse sentences. The topic and emotional keywords of the replies are pre-generated by introducing commonsense knowledge in the model. The reply is divided into multiple clauses, and then a multidimensional generator based on the transformer mechanism proposed in this paper is used to iteratively generate clauses from two dimensions, namely, sentence granularity and sentence structure. Subjective and objective experiments prove that compared with existing models, the proposed model effectively improves the semantic logic and emotional accuracy of replies. This model also significantly enhances the diversity of replies, largely overcoming the shortcomings of traditional models that generate safe replies.

A multi-label classication method using a hierarchical and transparent representation for paper-reviewer recommendation

Paper-reviewer recommendation problem in academic usually refers to recommend experts to comment on the quality of papers. How to effectively and accurately recommend reviewers for papers is a meaningful and still tough task. Generally, the representation of a paper and a reviewer is very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which slightly increases difficulty in paper-reviewer recommendation. In this paper, we propose a Multi-Label Classification method using a HIErarchical and transPArent Representation named Hiepar-MLC. Firstly, we introduce a HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers. Further we transform the paper-reviewer recommendation problem into a Multi-Label Classification (MLC) issue, whose multiple research labels exactly guide the learning process. It?s flexible that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Our experiments on the real dataset consists of the papers in ACM Digital Library show the effectiveness and feasibility of our method.

The Effects of Task Complexity on the Use of Different Types of Information in a Search Assistance Tool

Enhancing Personalized Recommendation by Implicit Preference Communities Modeling

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.

Next-Item Recommendation via Collaborative Filtering with Bidirectional Item Similarity

Exploiting temporal effect has empirically been recognized as a promising way to improve the recommendation performance in recent years. In real-world applications, one-class data in the form of (user, item, timestamp) are usually more accessible and abundant than numerical ratings. In this paper, we focus on exploiting such one-class data in order to provide personalized next-item recommendation services. Specifically, we base our work on the framework of time-aware item-based collaborative filtering, and propose a simple yet effective bidirectional item similarity (BIS) that is able to capture sequential patterns even from noisy data. Furthermore, we extend BIS via some factorization techniques and obtain an adaptive version, i.e., adaptive BIS (ABIS), in order to better fit the behavioral data. We also design a compound weighting function that leverages the complementarity between two well-known time-aware weighting functions. With the proposed similarity measurements and weighting function, we obtain two novel collaborative filtering methods that are able to achieve significantly better performance than the state-of-the-art methods, showcasing their effectiveness for next-item recommendation.

Investigating Searchers' Mental Models to Inform Search Explanations

Modern web search engines use many signals to select and rank results in response to queries. However, searchers' mental models of search are relatively unsophisticated, hindering their ability to use search engines efficiently and effectively. Annotating results with more in-depth explanations could help, but search engine providers need to know what to explain. To this end, we report on a study of searchers' mental models of web selection and ranking, with over 400 respondents to an online survey and 11 face-to-face interviews. Participants volunteered a range of factors and showed good understanding of important concepts such as popularity, wording, and personalisation. However, they showed little understanding of recency or diversity and incorrect ideas of payment for ranking. Where there are already explanatory annotations on the results page---such as "ad" markers and keyword highlighting---participants were familiar with ranking concepts. This suggests that further explanatory annotations may be useful.

A Deep Learning Architecture for Psychometric Natural Language Processing

Psychometric measures reflecting people's knowledge, ability, attitudes and personality traits are critical for many real-world applications, such as e-commerce, healthcare, and cybersecurity. However, traditional methods cannot collect and measure rich psychometric dimensions in a timely and unobtrusive manner. Consequently, despite their importance, psychometric dimensions have received limited attention from the natural language processing and information retrieval communities. In this paper, we propose a deep learning architecture, PyNDA, to extract psychometric dimensions from user-generated texts. PyNDA contains a novel representation embedding, a demographic embedding, a structural equation model (SEM) encoder, and a multi-task learning mechanism designed to work in unison to address the unique challenges associated with extracting rich, sophisticated and user-centric psychometric dimensions. Our experiments on three real-world datasets encompassing eleven psychometric dimensions, including trust, anxiety, and literacy, show that PyNDA markedly outperforms traditional feature-based classifiers as well as the state-of-the-art deep learning architectures. Ablation analysis reveals that each component of PyNDA significantly contributes to its overall performance. Collectively, the results demonstrate the efficacy of the proposed architecture for facilitating rich psychometric analysis. Our results have important implications for user-centric information extraction and retrieval systems looking to measure and incorporate psychometric dimensions.

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