Composed image retrieval is designed to more accurately retrieve target images that align with user intentions by using a combination of reference images and descriptive modification texts. However, existing methods primarily focus on designing complex feature fusion networks while neglecting the prevalent issues of noise and inconsistent sample quality in training data, leading to insufficient cross-modal semantic alignment and sample relevance modeling. To address this, we propose an innovative Tripartite Alignment Network (TAN) that introduces a momentum distillation mechanism, leveraging the historical knowledge of a teacher network as additional super-vision to guide the optimization of the student network. During the feature encoder fine-tuning stage, we design response-based knowledge distillation and feature-based knowledge distillation techniques, explicitly strengthening modal alignment through composed-target contrastive learning and implicitly promoting modal fusion via composed-target matching learning. In the combiner training stage, we incorporate a lightweight combiner network and employ a cross-entropy-based matching loss function, encouraging high matching scores for relevant image-text pairs and low scores for irrelevant pairs. Extensive experiments on the FashionIQ and Shoes datasets demonstrate that TAN exhibits superior performance compared to existing state-of-the-art methods, with notable improvements in R@10 of +14.03% and +13.09%, respectively. These results affirm the effectiveness of momentum distillation in multimodal learning. Access the source code at https://github.com/Maserhe/TAN.

Online social networks have found a significant increase in their popularity in recent years. All the networks have community structure, and one of the research problems mostly frequently tackled is the discovery of communities. An overlapping community is a network structure that allows one node to be a member of multiple communities. The method presented in this paper aims at detecting overlapping communities in social networks, and its novelty lies in that it combines with the Microblog User Model (MUM) which can reflect the interest of the user accurately. First, the MUM network, which is an undirected and weighted network, is constructed by computing the similarity among MUMs. Afterwords, Overlapping Community Detection based on MUM (OCD-MUM) is performed to partition the network. A community stops expanding when the fitness function reaches a local maximum. The communities detected are locally optimized. A user's interest is not only decided by the MUM, but it is also affected by the communities the user belongs to. The community model can reflect the interest of the community. The MUM is updated with community model of its communities, and therefore the interest of the user can be predicted by these communities. Our experiment result shows that OCD-MUM has a higher modularity Q value than traditional methods and the predicted interest is more close to the real world situations.

With the information overload in web services, micro-blog has been increasingly providing as a media for end-users to express their opinions. The notable feature of micro-blog articles is prone to be a burst of popularity during a short period. In addition, diverse interests make users bored in redundant items in most recommender systems. Therefore, providing users with diverse popular micro-blogs that suit their interesting topics is an important issue. In this paper, depending on forwarding number and comment number of micro-blogs, an effective model for popularity prediction is proposed to discover popular topics. Then, a MaxMin diversity algorithm based on content distance and popularity density is proposed to discover top k micro-blogs. Finally, we design a diverse personalized popularity attention (DPPA) recommendation approach for target user. We conduct extensive experiments on large scale micro-blog datasets. The experimental results show that our proposed approach can satisfy user's requirements with a higher recall than personal attention methods.