Distributed-Representation Based Hybrid Recommender System with Short Item Descriptions

He, Junhua; Zhuo, Hankz Hankui; Law, Jarvan

doi:10.48550/arxiv.1703.04854

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…We then compare the following baseline models in the experiment: (3) ReDial . It consists of a dialogue generation module based on HRED (Serban et al, 2017), a recommender module based on auto-encoder (He et al, 2017), and a sentiment analysis module. ( 4) KBRD (Chen et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph

Wang¹,

Hu²,

Sha³

et al. 2021

Preprint

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In this paper, we present a pre-trained language model (PLM) based framework called RID for conversational recommender system (CRS). RID finetunes the large-scale PLMs such as DialoGPT, together with a pretrained Relational Graph Convolutional Network (RGCN) to encode the node representations of an item-oriented knowledge graph. The former aims to generate fluent and diverse dialogue responses based on the strong language generation ability of PLMs, while the latter is to facilitate the item recommendation by learning better node embeddings on the structural knowledge base. To unify two modules of dialogue generation and item recommendation into a PLMs-based framework, we expand the generation vocabulary of PLMs to include an extra item vocabulary, and introduces a vocabulary pointer to control when to recommend target items in the generation process. Extensive experiments on the benchmark dataset REDIAL show RID significantly outperforms the state-of-the-art methods on both evaluations of dialogue and recommendation 1 .

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Section: Methodsmentioning

confidence: 99%

Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph

Wang¹,

Hu²,

Sha³

et al. 2021

Preprint

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“…• ReDial [13]: This model is proposed in the same paper with the ReDial dataset. It consists of a dialog generation module based on HRED [22] and a recommender module based on auto-encoder [7].…”

Section: Baselinesmentioning

confidence: 99%

C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

Zhou¹,

Zhou²,

Zhao³

et al. 2022

Preprint

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Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context. To address issue, a promising solution is to incorporate external data for enriching the context information. However, prior studies mainly focus on designing fusion models tailored for some specific type of external data, which is not general to model and utilize multi-type external data.To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarsegrained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion. Such an approach can be extended to incorporate more kinds of external data. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks. CCS CONCEPTS• Information systems → Users and interactive retrieval; Recommender systems.

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“…In technique, CRS can be seen as a content-based recommendation method, building user's profiles in the dialog component and matching them with item's attributes (like reviews [380]) in the recommendation component. The item-oriented method [383][384][385][386][387][388] for CRS is the most common-used one, uncovering hidden relations between items by employing knowledge graphs. Furthermore, Zhou et al [389] discovered that the word-level enrichment in conversations reveals user's personal habits in word usage and is valuable to understand user's preferences.…”

Section: Recommendation Involving Knowledgementioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

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Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

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Distributed-Representation Based Hybrid Recommender System with Short Item Descriptions

Cited by 5 publications

References 11 publications

Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph

Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph

C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

Recommender systems based on graph embedding techniques: A comprehensive review

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