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Comparing Twitter and Traditional Media Using Topic Models
TLDR
This paper empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling, and finds interesting and useful findings for downstream IR or DM applications.
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Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
TLDR
This paper investigates matching a response with its multi-turn context using dependency information based entirely on attention using Transformer in machine translation and extends the attention mechanism in two ways, which jointly introduce those two kinds of attention in one uniform neural network.
View on ACL
Heterogeneous Information Network Embedding for Recommendation
TLDR
A novel heterogeneous network embedding based approach for HIN based recommendation, called HERec is proposed, which shows the capability of the HERec model for the cold-start problem, and reveals that the transformed embedding information from HINs can improve the recommendation performance.
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Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid
TLDR
This paper proposes a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words and shows that with a relatively small amount of training data, this model can effectively identify aspect and opinion words simultaneously.
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S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
TLDR
This work proposes the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture, to utilize the intrinsic data correlation to derive self-supervision signals and enhance the data representations via pre-training methods for improving sequential recommendation.
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Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model
TLDR
A novel deep neural network with the co-attention mechanism for leveraging rich meta-path based context for top-N recommendation and performs well in the cold-start scenario and has potentially good interpretability for the recommendation results.
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Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
TLDR
This work incorporates both word-oriented and entity-oriented knowledge graphs~(KG) to enhance the data representations in CRSs, and adopts Mutual Information Maximization to align the word-level andentity-level semantic spaces.
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Topical Keyphrase Extraction from Twitter
TLDR
A context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking are proposed.
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Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks
TLDR
This paper proposes a novel knowledge enhanced sequential recommender that integrates the RNN-based networks with Key-Value Memory Network (KV-MN) and incorporates knowledge base information to enhance the semantic representation of KV- MN.
View on ACM
Towards Topic-Guided Conversational Recommender System
TLDR
This paper presents the task of topic-guided conversational recommendation, and proposes an effective approach to this task, and contributes a new CRS dataset named TG-ReDial (Recommendation through Topic-Guided Dialog), which has two major features.
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