This article presents a novel model named Adversarial Auto-encoder Domain Adaptation to handle the recommendation problem under cold-start settings. Specifically, we divide the hypergraph into two hypergraphs, i.e., a positive hypergraph and a negative one. Below, we adopt the cold-start user recommendation for illustration. After achieving positive and negative hypergraphs, we apply hypergraph auto-encoders to them to obtain positive and negative embeddings of warm users and items. Additionally, we employ a multi-layer perceptron to get warm and cold-start user embeddings called regular embeddings. Subsequently, for warm users, we assign positive and negative pseudo-labels to their positive and negative embeddings, respectively, and treat their positive and regular embeddings as the source and target domain data, respectively. Then, we develop a matching discriminator to jointly minimize the classification loss of the positive and negative warm user embeddings and the distribution gap between the positive and regular warm user embeddings. In this way, warm users’ positive and regular embeddings are connected. Since the positive hypergraph maintains the relations between positive warm user and item embeddings, and the regular warm and cold-start user embeddings follow a similar distribution, the regular cold-start user embedding and positive item embedding are bridged to discover their relationship. The proposed model can be easily extended to handle the cold-start item recommendation by changing inputs. We perform extensive experiments on real-world datasets for both cold-start user and cold-start item recommendations. Promising results in terms of precision, recall, normalized discounted cumulative gain, and hit rate verify the effectiveness of the proposed method.

Adversarial Auto-encoder Domain Adaptation for Cold-start Recommendation with Positive and Negative Hypergraphs

From the observation that users reading news tend to not click outdated news, we propose the notion of 'lifetime' of news, with two hypotheses: (i) news has a shorter lifetime, compared to other types of items such as movies or e-commerce products; (ii) news only competes with other news whose lifetimes have not ended, and which has an overlapping lifetime (i.e., limited competitions). By further developing the characteristics of the lifetime of news, then we present a novel approach for news recommendation, namely, Lifetime-Aware News reCommEndeR System (LANCER) that carefully exploits the lifetime of news during training and recommendation. Using real-world news datasets (e.g., Adressa and MIND), we successfully demonstrate that state-of-the-art news recommendation models can get significantly benefited by integrating the notion of lifetime and LANCER, by up to about 40% increases in recommendation accuracy.

/pdf/lancer-a-lifetime-aware-news-recommender-system-3iph8rx5.pdf

LANCER: A Lifetime-Aware News Recommender System

Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually utilize ID embeddings to represent items, which are not shared across different domains and lack the transferable ability. Recent studies use pre-trained language models (PLM) for item text embeddings (text-based IRL) that are universally applicable across domains. However, the existing text-based IRL is unaware of the important collaborative filtering (CF) information. In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation. To effectively incorporate CF information into text-based IRL, we convert the item-level interaction data to a word graph containing word-level collaborations. Subsequently, we design a novel pre-training task to align the word-level semantic- and CF-related item representation. Extensive experimental results on multiple public datasets demonstrate that compared to state-of-the-art transferable sequential recommenders, CoWPiRec achieves significantly better performances in both fine-tuning and zero-shot settings for cross-scenario recommendation and effectively alleviates the cold-start issue. The code is available at: https://github.com/ysh-1998/CoWPiRec.

Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation

Abstract Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users’ preferences and items’ characteristics for Recommender Systems (RSs). Most of the data in RSs can be organized into graphs where various objects (e.g. users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g. random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyse their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability, and so on. Finally, we share some potential research directions in this rapidly growing area. 

/pdf/recommending-on-graphs-a-comprehensive-review-from-a-data-3bxyjiq0.pdf

Recommending on graphs: a comprehensive review from a data perspective

Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually utilize ID embeddings to represent items, which are not shared across different domains and lack the transferable ability. Recent studies use pre-trained language models (PLM) for item text embeddings (text-based IRL) that are universally applicable across domains. However, the existing text-based IRL is unaware of the important collaborative filtering (CF) information. In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation. To effectively incorporate CF information into text-based IRL, we convert the item-level interaction data to a word graph containing word-level collaborations. Subsequently, we design a novel pre-training task to align the word-level semantic-and CF-related item representation. Extensive experimental results on multiple public datasets demonstrate that compared to state-of-the-art transferable sequential recommenders, CoWPiRec achieves significantly better performances in both fine-tuning and zero-shot settings for cross-scenario recommendation and effectively alleviates the cold-start issue. The code is available at: https://github.com/ysh-1998/CoWPiRec. 

News recommendation plays an indispensable role in acquiring daily news for users. Previous studies make great efforts to model high-order feature interactions between users and items, where various neural models are applied (e.g., RNN, GNN). However, we find that seldom efforts are made to get better representations for news. Most previous methods simply adopt pre-trained word embeddings to represent news and also suffer from cold-start users. In this work, we propose a new textual content representation method by building a word graph for recommendation, which is named WG4Rec. Three types of word associations are adopted in WG4Rec for content representation and user preference modeling, namely: 1)semantically-similar according to pre-trained word vectors, 2)co-occurrence in documents, and 3)co-click by users across documents. As extra information can be unified by adding nodes/edges to the word graph easily, WG4Rec is flexible to make use of cross-platform and cross-domain context for recommendation to alleviate the cold-start issue. To the best of our knowledge, it is the first attempt that using these relationships for news recommendation to better model textual content and adopt cross-platform information. Experimental results on two large-scale real-world datasets show that WG4Rec significantly outperforms state-of-the-art algorithms, especially for cold users in the online environment. Besides, WG4Rec achieves better performances when cross-platform information is utilized.

WG4Rec: Modeling Textual Content with Word Graph for News Recommendation

News recommendation, which aims to help users find the news they are interested in, is essential for online news platforms to alleviate the information overload problem. News is full of textual information with some knowledge entities, so recent studies try to leverage knowledge graphs (KGs) as side information to better model user preferences over news. However, most knowledge-enhanced methods assume that users are interested in the knowledge entities that occurred in the news. In real scenarios, users may like the news because of the news content rather than the knowledge entities. To take both knowledge and content factors into consideration, we propose a news recommendation method, namely knowledge and content aware network for news recommendation (KCNR). KCNR represents user and news in terms of knowledge and content, then it predicts the weight of user preferences on knowledge and content via a user preferences prediction mechanism. Besides, based on the weight of user preferences on knowledge, it extends user preferences along with entities in knowledge graphs. Experiments on two real-world datasets show that our approach achieves significant improvements over several state-of-the-art baselines in news recommendation.

Incorporating Knowledge and Content Information to Boost News Recommendation.

There are abundant user posts in social media which contain valuable information. Lots of previous studies focus on social media analytics, such as topic detection, sentiment prediction, and event trend analysis. According to psychological theories, namely affective forecasting, endowment effect, and negativity bias, user stance (one’s role in a specific social event, e.g. involvement) results in biased sentiment and attitude in real scenarios. However, user stance has not been taken into consideration in previous work. In most cases, user stance is a visible factor, so we argue that it should not be ignored. In this paper, we introduce user stance into two real scenarios (sentiment analysis and attitude prediction). Firstly, analyses on two real scenarios indicate that user stance does matter and provides more useful information for event analyses. Different user stance groups have significantly distinct sentiments and attitudes on an event (or a topic). By taking the differences into consideration, it is easy to get better mining results. Secondly, experimental results show that taking user stance information into account improves prediction results. Instead of designing a new algorithm, we propose that different algorithms should incorporate user’s stance information in online social event analysis. To the best of our knowledge, this is the first work which integrates psychological theories of user stance bias on understanding social events in the online environment.

Stance Influences Your Thoughts: Psychology-Inspired Social Media Analytics.

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WG4Rec: Modeling Textual Content with Word Graph for News Recommendation

Incorporating Knowledge and Content Information to Boost News Recommendation.

Stance Influences Your Thoughts: Psychology-Inspired Social Media Analytics.