Abstract: Most recommendation algorithms that use the relationship between tags and users and items have to face the problem of sparse tags caused by different individual users. Different users will have different tags for the items. Aiming at the problem of sparse user-tag and item-tag matrix due to the randomness of user labeling, a collaborative filtering recommendation algorithm based on tag extension is proposed. The label similarity based on the label is calculated according to the user's labeling behavior, and the label similarity based on the label semantics is calculated according to the semantics of the label marked by the user. The similarity of tags is evaluated in terms of user behavior and label semantics, and the tag similarity is used to expand each item-tag to reduce the sparseness of the matrix generated by the association relationship between items and tags. Experimental results show that running the algorithm on the dataset MovieLens improves the accuracy.

Key words: collaborative filtering, tag sparse, tag semantics, tag extension