As a kind of side information， Knowledge Graph （KG） can effectively improve the recommendation quality of recommendation models， but the existing knowledge-awareness recommendation methods based on Graph Neural Network （GNN） suffer from unbalanced utilization of node information. To address the above problem， a new recommendation method based on Knowledge?awareness and Cross-level Contrastive Learning （KCCL） was proposed. To alleviate the problem of unbalanced node information utilization caused by the sparse interaction data and noisy knowledge graph that deviate from the true representation of inter-node dependencies during information aggregation， a contrastive learning paradigm was introduced into knowledge-awareness recommendation model of GNN. Firstly， the user-item interaction graph and the item knowledge graph were integrated into a heterogeneous graph， and the node representations of users and items were realized by a GNN based on the graph attention mechanism. Secondly， consistent noise was added to the information propagation aggregation layer for data augmentation to obtain node representations of different levels， and the obtained outermost node representation was compared with the innermost node representation for cross-level contrastive learning. Finally， the supervised recommendation task and the contrastive learning assistance task were jointly optimized to obtain the final representation of each node. Experimental results on DBbook2014 and MovieLens-1m datasets show that compared to the second prior contrastive method， the Recall@10 of KCCL is improved by 3.66% and 0.66%， respectively， and the NDCG@10 is improved by 3.57% and 3.29%， respectively， which verifies the effectiveness of KCCL.

In Event-Based Social Network （EBSN）， the recommendation work starts from the user historical preferences to model user preferences， which hinders the scope and ways for users to access new things. Aiming at the above problems， an unexpectedness metric-based social event recommendation model was proposed， namely UER（Unexpectedness-based Event Recommendation）. UER model included two sub-models， Base and Unexpected. Firstly， based on the interaction sequence characteristics of users， events， and user historical events， the Base sub-model used the attention mechanism to measure the weights of events in user historical preferences， and finally predicted the probabilities of users participating in events. Secondly， multiple interest representations of the user were extracted by Unexpected sub-model through the self-attention mechanism to calculate the unexpectedness of the user itself and the unexpectedness value of the candidate event to the user according to the multiple interest representations of the user， so as to measure the unexpectedness of the recommended event. Experimental results on Meetup-California dataset show that compared with Deep Interest Network （DIN） and Personalized Unexpected Recommender System （PURS）， the recommendation Hit Ratio （HR） of the UER model is increased by 22.9% and 30.3%， the Normalized Discounted Cumulative Gain （NDCG） is increased by 27.5% and 42.3%， and the unexpectedness of recommended events is increased by 54.5% and 21.4% respectively. On Meetup-NewYork dataset， the recommendation HR of the UER model is increased by 18.2% and 21.8%， the NDCG is increased by 26.9% and 32.0%， and the unexpectedness of recommended events is increased by 52.6% and 20.8% respectively.