On the MOOC platform， there may be multiple versions of videos for one course，in order to recommend a MOOC video that satisfies the learning interest of the student，it is necessary to analyze the relationship between student interests and video contents. For this purpose， a video recommendation model named Mrec was proposed based on meta-path attention mechanism. For one thing， the Heterogeneous Information Network （HIN） was used to describe the relationships between learners and MOOC resources， and then meta-path was used to express the interaction between students and videos. For another， the attention mechanism was used to capture the influences of the characteristics of students， videos and meta-paths on learning interest. Specifically， the Mrec model was composed of two layers of attention mechanism： the first layer was the node attention layer， the node own characteristics were weightely combined with neighbor characteristics， and the feature representations of entities under different meta-paths were obtained by multi-head attention； the second layer was the path attention layer， in which the feature representations of entities learned under the guidance of different meta-paths were integrated to capture the feature representations of entities under different interests， and the learned users and video entities were put into Multi-Layer Perceptron （MLP） to obtain the prediction scores for top-K recommendation. Experimental results on MOOCCube and MOOCdata datasets show that Mrec outperforms the comparison methods in terms of Hit Ratio （HR）， Normalized Discounted Cumulative Gain （NDCG）， Mean Reciprocal Ranking （MRR） and Area Under receiver operating characteristic Curve （AUC）.

Due to the timeliness of activities in Event-Based Social Network （EBSN）， the traditional social network recommendation algorithms cannot be applied to EBSN. In addition， most of the traditional recommendation algorithms ignore the feedback that can affect whether the previous users accept the recommendation， which influences subsequent recommendation quality. Therefore， an activity recommendation method based on directed label graph and user feedback in EBSN was proposed. Firstly， EBSN was abstracted into a directed label graph， and a Directed Graph Structure Feature （DGSF） index was construction by extracting the property feature information of nodes and edges to filter nodes and edges for the first time. DGSF index consists of node property feature index， directed edge property feature index and time feature index. Secondly， a multi-attribute candidate set filtering strategy based on DGSF index was proposed. By using the limits of time， in-degrees and out-degrees of nodes， and label types， the further pruning of the candidate sets was realized to avoid redundant computation. Thirdly， an improved UCB （Upper Confidence Bound） activity recommendation algorithm with user feedback was put forward， namely EN_UCB （Elastic Net UCB）. In EN_UCB， with the introduction of the elastic net regression， the interest values of the user to the activities were calculated according to many influencing factors， and the activities with high interest values were recommended to the user. At the same time， the feedback whether the user accepted the activities was received to optimize the subsequent user recommendation. Experimental results show that EN_UCB has the accept rate higher than TS （Thompson Sampling）， UCB and eGreedy， the regret rate far lower than TS and eGreedy， the running time superior to TS， UCB and eGreedy， and the larger the number of activities， the more obvious the advantages. The proposed method implements online activity recommendation in EBSN effectively.