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ACM Transactions on

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Attentive Aspect Modeling for Review-Aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The... (more)

On Annotation Methodologies for Image Search Evaluation

Image search engines differ significantly from general web search engines in the way of presenting search results. The difference leads to different... (more)

Using Collection Shards to Study Retrieval Performance Effect Sizes

Despite the bulk of research studying how to more accurately compare the performance of IR systems, less attention is devoted to better understanding... (more)

Personalised Reranking of Paper Recommendations Using Paper Content and User Behavior

Academic search engines have been widely used to access academic papers, where users’ information needs are explicitly represented as search... (more)

Exploring High-Order User Preference on the Knowledge Graph for Recommender Systems

To address the sparsity and cold-start problem of collaborative filtering, researchers usually make use of side information, such as social networks... (more)

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling... (more)

Does Diversity Affect User Satisfaction in Image Search

Diversity has been taken into consideration by existing Web image search engines in ranking search results. However, there is no thorough... (more)

The Effects of Working Memory, Perceptual Speed, and Inhibition in Aggregated Search

Prior work has studied how different characteristics of individual users (e.g., personality traits and cognitive abilities) can impact search... (more)

NEWS

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.

 

About TOIS

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.

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Forthcoming Articles

Search Result Reranking with Visual and Structure Information Sources

Memory-augmented Dialogue Management for Task-oriented Dialogue Systems

Dialogue management (DM) decides the next action of a dialogue system according to the current dialogue state, and thus plays a central role in task-oriented dialogue systems. Since dialogue management requires to have access to not only local utterances, but also the global semantics of the entire dialogue session, modeling the long-range history information is a critical issue. To this end, we propose a novel Memory-Augmented Dialogue management model (MAD) which employs a memory controller and two additional memory structures, a slot-value memory and an external memory. The slot-value memory tracks the dialogue state by memorizing and updating the values of semantic slots (for instance, cuisine, price, and location), and the external memory augments the representation of hidden states of traditional recurrent neural networks through storing more context information. To update the dialogue state efficiently, we also propose slot-level attention on user utterances to extract specific semantic information for each slot. Experiments show that our model can obtain state-of-the-art performance and outperforms existing baselines.

Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification

Polylingual Text Classification (PLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when naively classifying each document via its corresponding language-specific classifier. In order to obtain an increase in the classification accuracy for a given language, the system thus needs to also leverage the training examples written in the other languages. We tackle multilabel PLC via funnelling, a new ensemble learning method that we propose here. Funnelling consists of generating a two-tier classification system where all documents, irrespectively of language, are classified by the same (2nd-tier) classifier. For this classifier all documents are represented in a common, language-independent feature space consisting of the posterior probabilities generated by 1st-tier, language-dependent classifiers. This allows the classification of all test documents, of any language, to benefit from the information present in all training documents, of any language. We present substantial experiments, run on publicly available polylingual text collections, in which funnelling is shown to significantly outperform a number of state-of-the-art baselines. All code and datasets (in vector form) are made publicly available.

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