The objective of cross-domain sequential recommendation is to forecast upcoming interactions by leveraging past interactions across diverse domains. Most methods aim to utilize single-domain and ...cross-domain information as much as possible for personalized preference extraction and effective integration. However, on one hand, most models ignore that cross-domain information is composed of multiple single-domains when generating representations. They still treat cross-domain information the same way as single-domain information, resulting in noisy representation generation. Only by imposing certain constraints on cross-domain information during representation generation can subsequent models minimize interference when considering user preferences. On the other hand, some methods neglect the joint consideration of users’ long-term and short-term preferences and reduce the weight of cross-domain user preferences to minimize noise interference. To better consider the mutual promotion of cross-domain and single-domains factors, we propose a novel model (C2DREIF) that utilizes Gaussian graph encoders to handle information, effectively constraining the correlation of information and capturing useful contextual information more accurately. It also employs a Top-down transformer to accurately extract user intents within each domain, taking into account the user’s long-term and short-term preferences. Additionally, entropy regularized is applied to enhance contrastive learning and mitigate the impact of randomness caused by negative sample composition.
•A Gaussian distribution-based graph encoder strengthens cross-domain constraints.•A Top-down transformer is used to capture users’ authentic intentions.•An entropy loss is introduced to enhance contrastive learning.
Mobile payment systems offer enormous potential as alternative payment solutions. However, the diffusion of mobile payments over the years has been less than optimal despite the numerous studies that ...have explored the reasons for its adoption. Consequently, there is an increased interest in exploring alternative actions for promoting its diffusion, especially user recommendation of the technology. This is because positive recommendations can enormously influence the decisions of potential consumers to use the technology while negative recommendations can increase resistance to it. The few extant studies in this domain have followed the traditional survey approach with hypothetic-deductive reasoning, thus limiting an understanding of factors outside their conceptual models that could influence recommendations. To address this shortcoming, this study uses a qualitative text-mining approach that explores themes from user reviews of mobile payment applications (apps). Using 5955 reviews from 16 mobile payment apps hosted on the Google Play store, this study applied the latent Dirichlet allocation (LDA) text-mining method to extract themes from the reviews that help to explain why users provide positive or negative recommendations about mobile payment systems. A total of 13 themes (i.e. ease of use, usefulness, convenience, security, reliability, satisfaction, transaction speed, time-saving, customer support, output quality, perceived cost, usability and trust) were generated from the LDA model which provides both theoretical and practical insights for advancing mobile payments diffusion and research.
Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential ...recommendation. Existing works on Shared-account Cross-domain Sequential Recommendation (SCSR) are mainly based on Recurrent Neural Network (RNN) and Graph Neural Network (GNN) but they ignore the fact that although multiple users share a single account, it is mainly occupied by one user at a time. This observation motivates us to learn a more accurate user-specific account representation by attentively focusing on its recent behaviors. Furthermore, though existing works endow lower weights to irrelevant interactions, they may still dilute the domain information and impede the cross-domain recommendation. To address the above issues, we propose a reinforcement learning-based solution, namely RL-ISN, which consists of a basic cross-domain recommender and a reinforcement learning-based domain filter. Specifically, to model the account representation in the shared-account scenario, the basic recommender first clusters users' mixed behaviors as latent users, and then leverages an attention model over them to conduct user identification. To reduce the impact of irrelevant domain information, we formulate the domain filter as a hierarchical reinforcement learning task, where a high-level task is utilized to decide whether to revise the whole transferred sequence or not, and if it does, a low-level task is further performed to determine whether to remove each interaction within it or not. To evaluate the performance of our solution, we conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our RL-ISN method compared with the state-of-the-art recommendation methods.
Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative ...paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on four real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method.
The graph-based recommendation systems achieve significant success, yet they are accompanied by malicious attacks. In most scenes, attackers will inject crafted fake profiles into the recommendation ...system to boost the ranking of their target items in the recommendation lists. For an exploration of potential attacks hidden in real life, researchers have proposed various attacks with severe threats. However, current efforts in exploring attack strategies neglect the stealthiness aspect of the attack, i.e., the perturbation of recommendation performance after an attack can be quite noticeable, potentially alerting the defenders. To fill this research gap, this paper introduces a novel attack framework named InfoAtk, designed to conduct attacks while ensuring stealthiness. Specifically, given the dependency of precise recommendation predominantly on representations, the framework employs contrastive learning techniques to align representations before and after the attack, thereby augmenting stealthiness. Additionally, we optimize the representation of target items to outrank the last items in users’ recommendation lists, thereby promoting the visibility of the target item to increase the attack’s effectiveness. Extensive experiments on four public datasets validate the stealthiness and effectiveness of our proposed attack framework.
•We explore the stealthiness of recommender attack to fill this research gap.•We introduce a poisoning attack method based on a bi-level optimization framework.•We leverage contrastive learning to align item embeddings for stealthiness.•We boost attack effectiveness with minimum cost by optimizing target item embedding.•Our experiments show the stealthiness and effectiveness of our attack.
With the widespread popularization of social network platforms, user-generated content and other social network data are growing rapidly. It is difficult for social users to select interested ...contents from the numerous social data. To alleviate information overload problem and enhance overall user experience of social networks, recommendation systems relying on historical behavioural data and social friendship relations of users, are widely used in social networks. Although researches on social recommendations have been conducted in recent years, recommendation systems of social networks still suffer from several challenges, such as data sparsity and lower performance. Since graph neural network has huge advantages in graph data learning by aggregating neighbors representations of the central node, it has been gathering pace in recent years. In this survey, we review graph neural network based literature for solving recommendation problems in social networks. We first introduce backgrounds of graph neural network and recommendation systems in social networks. Then, for different types of recommendation problems in social networks, we review different graph neural network based recommendation methods briefly. In particular, we first review GNN-based methods for general social recommendation and then review GNN-based methods for different social recommendation scenarios (such as friend recommendation and point-of-interest recommendation). Finally, we briefly discuss promising future directions of the graph neural network based recommendation in social networks.
Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to ...answer two important questions well due to inherent shortcomings: (a) What exactly does a user like? (b) Why does a user like an item? The shortcomings are due to the way that static models learn user preference, i.e., without explicit instructions and active feedback from users. The recent rise of conversational recommender systems (CRSs) changes this situation fundamentally. In a CRS, users and the system can dynamically communicate through natural language interactions, which provide unprecedented opportunities to explicitly obtain the exact preference of users. Considerable efforts, spread across disparate settings and applications, have been put into developing CRSs. Existing models, technologies, and evaluation methods for CRSs are far from mature. In this paper, we provide a systematic review of the techniques used in current CRSs. We summarize the key challenges of developing CRSs in five directions: (1) Question-based user preference elicitation. (2) Multi-turn conversational recommendation strategies. (3) Dialogue understanding and generation. (4) Exploitation-exploration trade-offs. (5) Evaluation and user simulation. These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI). Based on these research directions, we discuss some future challenges and opportunities. We provide a road map for researchers from multiple communities to get started in this area. We hope this survey can help to identify and address challenges in CRSs and inspire future research.
With the expansion of the online environment, recently, recommendation systems have become established as an essential element of any online service. Following this trend, the issue of how to present ...recommended information effectively to users is attracting attention not only in companies but also in the research field. This paper presents a between-subjects study that aimed to elicit whether there is a difference in consumer’s attitudes towards the user-centric and content-centric recommendation approaches based on their level of psychological ownership towards the online service. Our findings indicated that users with high psychological ownership toward the online service prefer the user-centric recommendation approach, while users with low psychological ownership prefer the content-centric recommendation approach.
In this study, we show that individual users’ preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate ...that we can extract strong signals about individual preferences for recommendation diversity, popularity and serendipity by measuring their personality traits. We conducted an online experiment with over 1,800 users for six months on a live recommendation system. In this experiment, we asked users to evaluate a list of movie recommendations with different levels of diversity, popularity, and serendipity. Then, we assessed users’ personality traits using the Ten-item Personality Inventory (TIPI). We found that ratings-based recommender systems may often fail to deliver preferred levels of diversity, popularity, and serendipity for their users (e.g. users with high-serendipity preferences). We also found that users with different personalities have different preferences for these three recommendation properties. Our work suggests that we can improve user satisfaction when we integrate users’ personality traits into the process of generating recommendations.
We frequently rely on suggestions in an online context, including those from search engine results, e-commerce product recommendations, movie recommendations, and so forth. These suggestions are made ...in response to our actions, such as when we search for products, songs, or movies, or at the very least, when we visit the website in order to receive a recommendation. These recommendations do not take into account time-sensitive factors, such as when we often utilise these services during the day or the week. We prefer to repeat some activities, like listening to music at a specific time of day. We presented a methodology in this paper that addresses two critical issues in personalised recommendation. 1) recommending the right items to the right people at the right time, and 2) estimating when a user will return to a service or product after performing repeated actions. The scholarly community has not yet examined this work in personalised recommendations. To do this, we offered the Hawkes process, in which we employed a customised initial intensity based on Hierarchical Poisson Factorization, and for a dynamic activity, we considered a sinusoidal function coupled with exponential effect decay. This is consistent with user activity cycles, such as music listening during a specific time of day. This is the first framework we've used, which is based on a probabilistic matrix factorization
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