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Contextual Path Retrieval: A Contextual Entity Relation Embedding-based Approach
Contextual path retrieval (CPR) refers to the task of finding contextual path(s) between a pair of entities in a knowledge graph that explains the connection between them in a given context. For this novel retrieval task, we propose the Embedding-based ...
Generating Relevant and Informative Questions for Open-Domain Conversations
Recent research has highlighted the importance of mixed-initiative interactions in conversational search. To enable mixed-initiative interactions, information retrieval systems should be able to ask diverse questions, such as information-seeking, ...
Interaction-aware Drug Package Recommendation via Policy Gradient
Recent years have witnessed the rapid accumulation of massive electronic medical records, which highly support intelligent medical services such as drug recommendation. However, although there are multiple interaction types between drugs, e.g., synergism ...
KR-GCN: Knowledge-Aware Reasoning with Graph Convolution Network for Explainable Recommendation
Incorporating knowledge graphs (KGs) into recommender systems to provide explainable recommendation has attracted much attention recently. The multi-hop paths in KGs can provide auxiliary facts for improving recommendation performance as well as ...
User Behavior Simulation for Search Result Re-ranking
Result ranking is one of the major concerns for Web search technologies. Most existing methodologies rank search results in descending order of relevance. To model the interactions among search results, reinforcement learning (RL algorithms have been ...
Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations
The sequential recommendation (also known as the next-item recommendation), which aims to predict the following item to recommend in a session according to users’ historical behavior, plays a critical role in improving session-based recommender systems. ...
Hierarchical Sliding Inference Generator for Question-driven Abstractive Answer Summarization
Text summarization on non-factoid question answering (NQA) aims at identifying the core information of redundant answer guidance using questions, which can dramatically improve answer readability and comprehensibility. Most existing approaches focus on ...
Reinforcement Routing on Proximity Graph for Efficient Recommendation
We focus on Maximum Inner Product Search (MIPS), which is an essential problem in many machine learning communities. Given a query, MIPS finds the most similar items with the maximum inner products. Methods for Nearest Neighbor Search (NNS) which is ...
Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction
Relation ties, defined as the correlation and mutual exclusion between different relations, are critical for distant supervised relation extraction. Previous studies usually obtain this property by greedily learning the local connections between ...
Sequential Recommendation with Multiple Contrast Signals
Sequential recommendation has become a trending research topic for its capability to capture dynamic user intents based on historical interaction sequence. To train a sequential recommendation model, it is a common practice to optimize the next-item ...
Revisiting Negative Sampling vs. Non-sampling in Implicit Recommendation
Recommendation systems play an important role in alleviating the information overload issue. Generally, a recommendation model is trained to discern between positive (liked) and negative (disliked) instances for each user. However, under the open-world ...
Characterization and Prediction of Mobile Tasks
Mobile devices have become an increasingly ubiquitous part of our everyday life. We use mobile services to perform a broad range of tasks (e.g., booking travel or conducting remote office work), leading to often lengthy interactions with several distinct ...
Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation
Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have ...
A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation
Recently, research on open domain dialogue systems have attracted extensive interests of academic and industrial researchers. The goal of an open domain dialogue system is to imitate humans in conversations. Previous works on single turn conversation ...
Users Meet Clarifying Questions: Toward a Better Understanding of User Interactions for Search Clarification
The use of clarifying questions (CQs) is a fairly new and useful technique to aid systems in recognizing the intent, context, and preferences behind user queries. Yet, understanding the extent of the effect of CQs on user behavior and the ability to ...
Knowledge-Enhanced Attributed Multi-Task Learning for Medicine Recommendation
Medicine recommendation systems target to recommend a set of medicines given a set of symptoms which play a crucial role in assisting doctors in their daily clinics. Existing approaches are either rule-based or supervised. However, the former heavily ...
The Influences of a Knowledge Representation Tool on Searchers with Varying Cognitive Abilities
While current systems are effective in helping searchers resolve simple information needs (e.g., fact-finding), they provide less support for searchers working on complex information-seeking tasks. Complex search tasks involve a wide range of (meta)...
Curriculum Pre-training Heterogeneous Subgraph Transformer for Top-N Recommendation
To characterize complex and heterogeneous side information in recommender systems, the heterogeneous information network (HIN) has shown superior performance and attracted much research attention. In HIN, the rich entities, relations, and paths can be ...
A Multi-channel Hierarchical Graph Attention Network for Open Event Extraction
Event extraction is an essential task in natural language processing. Although extensively studied, existing work shares issues in three aspects, including (1) the limitations of using original syntactic dependency structure, (2) insufficient ...
Integrating Representation and Interaction for Context-Aware Document Ranking
Recent studies show that historical behaviors (such as queries and their clicks) contained in a search session can benefit the ranking performance of subsequent queries in the session. Existing neural context-aware ranking models usually rank documents ...
Few-shot Aspect Category Sentiment Analysis via Meta-learning
Existing aspect-based/category sentiment analysis methods have shown great success in detecting sentiment polarity toward a given aspect in a sentence with supervised learning, where the training and inference stages share the same pre-defined set of ...
perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online Games
Online games make up the largest segment of the booming global game market in terms of revenue as well as players. Unlike games that sell games at one time for profit, online games make money from in-game purchases by a large number of engaged players. ...
Personalized News Recommendation: Methods and Challenges
Personalized news recommendation is important for users to find interesting news information and alleviate information overload. Although it has been extensively studied over decades and has achieved notable success in improving user experience, there are ...
Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets
We propose a simple model of an idealized online cultural market in which N items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items’ popularity. Our goal is to better understand the ...