Social recommendation aims to improve recommendation effect of traditional recommendation algorithms by integrating social relations. Currently， social recommendation algorithms based on Network Embedding （NE） face two problems： one is that inconsistency between objects is not considered when constructing network， and algorithms are often restricted by positive objects that are difficult to obtain and have many constraints； the other is that the elimination of overfitting in algorithm training process based on the number of ratings cannot be realized by these algorithms. Therefore， an Adaptive Social Recommendation algorithm based on Negative Similarity （ASRNS） was proposed. Firstly， homogeneous networks with positive correlations were constructed by consistency analysis. Then， embedded vectors were obtained by combining weighted random walk with Skip-Gram algorithm. Next， similarities were calculated， and Matrix Factorization （MF） algorithm was constrained from the perspective of negative similarity. Finally， the number of ratings was mapped to the ideal rating range based on adaptive mechanism， and different penalties were imposed on bias terms of the algorithm. Experiments were conducted on FilmTrust and CiaoDVD datasets. The results show that compared with algorithms such as Collaborative User Network Embedding （CUNE） algorithm and Consistent neighbor aggregation for Recommendation （ConsisRec） algorithm， ASRNS has the Root Mean Square Error （RMSE） reduced by at least 2.60% and 5.53% respectively， and the Mean Absolute Error （MAE） reduced by at least 1.47% and 2.46% respectively. It can be seen that ASRNS can not only reduce rating prediction error effectively， but also improve over-fitting problem in algorithm training process significantly， and has good robustness for objects with different ratings.

To deal with the problem that the existing reviewer recommendation algorithms assign reviewers only through affinity score and ignore the research direction matching between reviewers and papers to be reviewed， a reviewer recommendation algorithm based on Affinity and Research Direction Coverage （ARDC） was proposed. Firstly， the order of the paper’s selection of reviewers was determined according to the frequencies of the research directions appearing in the papers and the reviewer’s paper groups. Secondly， the reviewer’s comprehensive review score to the paper to be reviewed was calculated based on the affinity score between the reviewers and the paper to be reviewed and the research direction coverage score of the reviewers to the paper to be reviewed， and the pre-assigned review team for the paper was obtained on the basis of round-robin scheduling. Finally， the final recommendation of the review team was realized based on the conflict of interest conflict inspection and resolution. Experimental results show that compared with assignment based reviewer recommendation algorithms such as Fair matching via Iterative Relaxation （FairIR） and Fair and Accurate reviewer assignment in Peer Review （PR4A）， the proposed algorithm has the average research direction coverage score increased by 38% on average at the expense of a small amount of affinity score， so that the recommendation result is more accurate and reasonable.

Focused on the issue that the most existing social recommendation algorithms ignore the influence of the association relationship between items on the recommendation accuracy， and fail to effectively combine user ratings with trust data， a Social recommendation algorithm combing Trust implicit similarity and Score similarity （SocialTS） was proposed. Firstly， the score similarity and trust implicit similarity between users were combined linearly to obtain reliable similar friends among users. Then， the trust relationship was integrated into the correlation analysis of items， and the modified similar items were obtained. Finally， similar users and items were added to the Matrix Factorization （MF） model as regularization terms， thereby obtaining more accurate feature representations of users and items. Experimental results show that on FilmTrust and CiaoDVD datasets， when the latent feature dimension is 10， compared with the mainstream social recommendation algorithm Trust-based Singular Value Decomposition （TrustSVD）， SocialTS has the Root Mean Square Error （RMSE） reduced by 4.23% and 8.38% respectively， and the Mean Absolute Error （MAE） reduced by 4.66% and 6.88% respectively. SocialTS can not only effectively improve users' cold start problem， but also accurately predict users' actual ratings under different numbers of ratings， and has good robustness.