SECTION: Special Section on Graph Technologies for User Modeling and Recommendation - Part 2

Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search
July 2022, Article No.: 43, pp 1–29

Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile ...

Dual Gated Graph Attention Networks with Dynamic Iterative Training for Cross-Lingual Entity Alignment
July 2022, Article No.: 44, pp 1–30

Cross-lingual entity alignment has attracted considerable attention in recent years. Past studies using conventional approaches to match entities share the common problem of missing important structural information beyond entities in the modeling process. ...

Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks
July 2022, Article No.: 46, pp 1–21

Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them ...

eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks
July 2022, Article No.: 47, pp 1–29

With the development of e-commerce, fraud behaviors have been becoming one of the biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking system of e-commerce platforms and adversely influence the shopping experience of ...

Graph Neural Collaborative Topic Model for Citation Recommendation
July 2022, Article No.: 48, pp 1–30

Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on ...

Open Access
Modeling Global and Local Interactions for Online Conversation Recommendation
July 2022, Article No.: 49, pp 1–33

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users ...

BotSpot++: A Hierarchical Deep Ensemble Model for Bots Install Fraud Detection in Mobile Advertising
July 2022, Article No.: 50, pp 1–28

Mobile advertising has undoubtedly become one of the fastest-growing industries in the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. Fraudsters can leverage many techniques, where bots install fraud is the ...

Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search Personalization
July 2022, Article No.: 51, pp 1–24

In this article, we propose a Latent Dirichlet Allocation– (LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user ...

Social Context-aware Person Search in Videos via Multi-modal Cues
July 2022, Article No.: 52, pp 1–25

Person search has long been treated as a crucial and challenging task to support deeper insight in personalized summarization and personality discovery. Traditional methods, e.g., person re-identification and face recognition techniques, which profile ...

I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts
July 2022, Article No.: 53, pp 1–30

Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to ...

LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective Recommendation
July 2022, Article No.: 54, pp 1–28

Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item ...

Open Access
Personalizing Medication Recommendation with a Graph-Based Approach
July 2022, Article No.: 55, pp 1–23

The broad adoption of electronic health records (EHRs) has led to vast amounts of data being accumulated on a patient’s history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information ...

SECTION: Regular Papers

Truncated Models for Probabilistic Weighted Retrieval
July 2022, Article No.: 56, pp 1–24

Existing probabilistic retrieval models do not restrict the domain of the random variables that they deal with. In this article, we show that the upper bound of the normalized term frequency (tf) from the relevant documents is much smaller than the upper ...

Embedding Hierarchical Structures for Venue Category Representation
July 2022, Article No.: 57, pp 1–29

Venue categories used in location-based social networks often exhibit a hierarchical structure, together with the category sequences derived from users’ check-ins. The two data modalities provide a wealth of information for us to capture the semantic ...

Hyperspherical Variational Co-embedding for Attributed Networks
July 2022, Article No.: 58, pp 1–36

Network-based information has been widely explored and exploited in the information retrieval literature. Attributed networks, consisting of nodes, edges as well as attributes describing properties of nodes, are a basic type of network-based data, and are ...

Multimodal Web Page Segmentation Using Self-organized Multi-objective Clustering
July 2022, Article No.: 59, pp 1–49

Web page segmentation (WPS) aims to break a web page into different segments with coherent intra- and inter-semantics. By evidencing the morpho-dispositional semantics of a web page, WPS has traditionally been used to demarcate informative from non-...

A Game Theory Approach for Estimating Reliability of Crowdsourced Relevance Assessments
July 2022, Article No.: 60, pp 1–29

In this article, we propose an approach to improve quality in crowdsourcing (CS) tasks using Task Completion Time (TCT) as a source of information about the reliability of workers in a game-theoretical competitive scenario. Our approach is based on the ...

Component-based Analysis of Dynamic Search Performance
July 2022, Article No.: 61, pp 1–47

In many search scenarios, such as exploratory, comparative, or survey-oriented search, users interact with dynamic search systems to satisfy multi-aspect information needs. These systems utilize different dynamic approaches that exploit various user ...

A Comparison between Term-Independence Retrieval Models for Ad Hoc Retrieval
July 2022, Article No.: 62, pp 1–37

In Information Retrieval, numerous retrieval models or document ranking functions have been developed in the quest for better retrieval effectiveness. Apart from some formal retrieval models formulated on a theoretical basis, various recent works have ...

An Unsupervised Aspect-Aware Recommendation Model with Explanation Text Generation
July 2022, Article No.: 63, pp 1–29

Review based recommendation utilizes both users’ rating records and the associated reviews for recommendation. Recently, with the rapid demand for explanations of recommendation results, reviews are used to train the encoder–decoder models for explanation ...



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