As an effective method to solve the cold start problem of recommender systems, cross-domain recommendation has received more and more attention and research. Currently, most cross-domain recommendation models rely on the unidirectional knowledge transfer between the same users in the source and target domains. The rich information in the source domain is transferred to the target domain with sparse information to achieve better recommendation effect. However, the performance of cross-domain recommendation heavily depends on the number of overlapping active users, and these models cannot fully utilize the useful knowledge behind active users in a single domain. This limitation makes it difficult for the model to achieve ideal results in real-world scenarios. To solve the above problems and optimize cross-domain recommendation, we propose a Two-way Cross-domain Recommendation with Central Social Influence(CST-CDR) and the concept of super-user. Through the idea of clustering, user circles are formed centering on active super-users, so the potential common preferences of single-field active users, dual-field active users, and super-users are fully explored, alleviating the dependence of the model on overlapping and active users. At the same time, the cross-domain recommendation is extended to realize two-way information migration, so that users who are active in only one domain can have a preliminary preference judgment. Finally, the effectiveness of the proposed method is demonstrated on two real datasets.
Machine translation technologies have the potential to bridge knowledge gaps across languages, promoting more inclusive access to information regardless of native languages. This study examines the impact of integrating Google Translate into Wikipedia's Content Translation system in January 2019. Employing a natural experiment design and difference-in-differences strategy, we analyze how this translation technology shock influenced the dynamics of content production and accessibility on Wikipedia across over a hundred languages. We find that this technology integration lead to a 149% increase in content production through translation, driven by existing editors become more productive as well as an expansion of the editor base. Moreover, we observe that machine translation enhances the propagation of biographical and geographical information, helping to close these knowledge gaps in the multilingual context. However, our findings also underscore the need for continued efforts to mitigate the preexisting systemic barriers. Our study contributes to our knowledge on the evolving role of artificial intelligence in shaping knowledge dissemination through enhanced language translation capabilities.
Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks. However, they ignore that the difference relation density of nodes between the user- and item-side causes the adaptability of graphs on bilateral nodes to be different after multi-hop graph interaction calculation, which limits existing models to achieve ideal results. To solve this issue, we propose a novel framework for recommendation tasks called Bilateral Unsymmetrical Graph Contrastive Learning (BusGCL) that consider the bilateral unsymmetry on user-item node relation density for sliced user and item graph reasoning better with bilateral slicing contrastive training. Especially, taking into account the aggregation ability of hypergraph-based graph convolutional network (GCN) in digging implicit similarities is more suitable for user nodes, embeddings generated from three different modules: hypergraph-based GCN, GCN and perturbed GCN, are sliced into two subviews by the user- and item-side respectively, and selectively combined into subview pairs bilaterally based on the characteristics of inter-node relation structure. Furthermore, to align the distribution of user and item embeddings after aggregation, a dispersing loss is leveraged to adjust the mutual distance between all embeddings for maintaining learning ability. Comprehensive experiments on two public datasets have proved the superiority of BusGCL in comparison to various recommendation methods. Other models can simply utilize our bilateral slicing contrastive learning to enhance recommending performance without incurring extra expenses.
How can a platform capture the value it creates for its users while fostering a diverse and inclusive marketplace? This study explores the potential consequences of monetizing a prominent promotional program within a platform market for book promotions, specifically the Giveaways program on Goodreads.com. Participation in this program was free until January 2018, when Goodreads introduced a policy that imposed a flat fee on authors or publishers who wish to promote their books in this program. We employ large-scale, fine-grained data to examine responses from both the supply side and the demand side of the two-sided market to this monetization strategy. Our results suggest that this policy results in a more concentrated book supply in the promotional market, increasing the market share of major and established publishers and authors at the expense of smaller entities with fewer resources. The policy also reduces book diversity in the marketplace, with a few popular genres becoming more dominant while niche genres diminish further. These supply-side changes ultimately impact consumers, as the policy exacerbates the effect of price promotion on word of mouth (i.e., increased review volume but lower valence). An analysis of textual reviews and rating dispersion reveals that consumer-book mismatch drives this consumer response. Our findings carry policy implications for platform owners and policymakers: to establish and maintain a healthy ecosystem, platforms need to mitigate potential undesirable effects from monetization strategies by implementing more flexible and nuanced incentive structures for various participants.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
