From Word Embeddings to Item Recommendation
TABLE I: Comparison of methods
['Method', 'Precision', 'Ndcg', 'HitRate', 'Coverage']
The first method from the recommendation literature is traditional collaborative filtering method (CF-C) which uses the past preferences of the users and their similarities. We implemented this method to observe its performance. The second method is Random, which randomly chooses k-many items (venues) to recommend. We run Random method 10 times and presented the average of the results. the last group are the methods that combine multiple features to make recommendation. Among the proposed methods, the best performing method is KNI which uses continuous vector representation modeled by skip-gram technique. NN performs poorer than the others, which indicates that recommending items that are only used by neighbors is not able to capture the preference of the target user, and use of item similarity to the target user is more promising. The random method’s poor performance show that making venue recommendation on CheckinsJan dataset is challenging, since there are many items to recommend, e.g. nearly 50000 venues.