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)

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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.

ACM Transactions on Information Systems (TOIS) is a scholarly journal that publishes previously unpublished high-quality scholarly articles in all areas of information retrieval.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
Improving Implicit Recommender Systems with Auxiliary Data

Most existing recommender systems leverage the primary feedback only, despite the fact that users also generate a large amount of auxiliary feedback. In this work, we improve implicit feedback based recommender systems (dubbed as Implicit Recommender Systems) by integrating auxiliary view data into matrix factorization (MF). To exploit di erent preference levels, we propose both point-wise and pairwise models in terms of how to leverage users? viewing behaviors. The latter model learns the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more e ective and exible than the former point-wise MF method. However, such a pairwise formulation poses computational e ciency problem in learning the model. To address this problem, we design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) learner. Notably, our designed algorithm can e ciently learn model parameters from the whole user-item matrix (including all missing data), with a rather low time complexity that is dependent on the observed data only. Extensive experiments on two real-world datasets demonstrate that our method outperforms several state-of-the-art MF methods by 6.8% ? 10%. Our implementation is available at: https: //github.com/dingjingtao/View_enhanced_ALS

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.

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

In interactive information retrieval, an important question is: How do task characteristics influence users' needs and behaviors? We report on a laboratory study that investigated the effects of task complexity on the types of information used by participants while searching. Participants completed tasks of four complexity levels and had access to four different types of information provided through a search-assistance tool referred to as the InfoBoxes (IB). The IB tool presented the following types of task-related information (\emph{info-types}) on different tabs: (1) facts, (2) concepts, (3) opinions, and (4) insights. Facts (and opinions) were defined as objective (and subjective) statements relevant to the task. Concepts were defined as important ideas, principles, or entities related to the task. Insights were defined as tips or advice about the task. The study investigated six research questions that considered the effects of task complexity on: (RQ1) participants' pre-/post-task perceptions about useful info-types; (RQ2) use of different info-types during the task; (RQ3) motivations for engaging with the IB; (RQ4) gains from using it; (RQ5) the search stage participants' were in while engaging with the IB; and (RQ6) motivations for sometimes avoiding the IB. Our results suggest that task complexity influenced all six types of outcomes.

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 Price-Per-Attention Auction Scheme Using Mouse Cursor Information

Payments in online ad auctions are typically derived from click-through rates, so that advertisers do not pay for ineffective ads. But advertisers often care about more than just clicks (that is, for example, if they aim to raise brand awareness or visibility). There is thus an opportunity to devise a more effective ad pricing paradigm, in which ads are paid only if they are actually noticed. This article contributes with a novel auction format based on a pay-per-attention (PPA) scheme. We show that the PPA auction inherits the desirable properties (strategy-proofness and efficiency) as its pay-per-impression and pay-per-click counterparts, and that it also compares favourably in terms of revenues. To make the PPA format feasible, we also contribute with a scalable diagnostic technology to predict user attention to ads in sponsored search using raw mouse cursor coordinates only, regardless of the page content and structure. We use the user attention predictions in numerical simulations to evaluate the PPA auction scheme. Our results show that, in relevant economic settings, the PPA revenues would be strictly higher than the existing auction payment schemes.

Efficient Neural Matrix Factorization without Sampling for Recommendation

Recently, deep learning has revolutionized many research fields and there is a surge of interest in applying it for recommendation. However, existing studies have largely focused on exploring complex deep learning architectures for recommendation task, while typically apply the negative sampling strategy for model learning. Despite effectiveness, we argue that these methods suffer from two important limitations: 1) the methods with complex network structures require expensive computations even with a sampling-based learning strategy; 2) the negative sampling strategy is not robust, making sampling-based methods difficult to achieve the optimal performance in practical applications. In this work, we propose to learn neural recommendation models from the whole data without sampling. However, such a non-sampling strategy poses strong challenges to learning efficiency. To address this, we derive three new optimization methods, which can efficiently learn model parameters from the whole data with a rather low time complexity. Moreover, we present a general framework (ENMF) based on a simple Neural Matrix Factorization architecture. Extensive experiments on three real-world datasets indicate that the proposed ENMF consistently and significantly outperforms the state-of-the-art methods on the Top-K recommendation task. Remarkably, ENMF also shows significant advantages in training efficiency, making it more applicable to real-world large-scale systems.

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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