Abstract:The existing Point Of Interest (POI) recommendation method operates based on the POI access frequency of an individual user and the access habits of his/her partakers in the location-based social network, with the geographical location of the POI as one of the recommendation conditions. However, most of the POI recommendations only take the geographical location of POI as the preferential reference, rather than the access cost of the users. Therefore, some of the POI candidates generated based on similar methods may meet the user preference but have poor accessibility. To solve the above problems, this study proposes a POI recommendation method based on LSTM and distance optimization. This method supplements the interaction matrix between a user and POI according to the user’s social network and then decomposes the matrix into the hidden vectors of the POI. Finally, according to the user’s POI access record, the temporal relationship between the hidden vectors is established, and the sequence is learned in a recursion-like model to infer the possible POI sequence accessed by the user in the future. In addition, experiments on the Gowalla and Yelp data sets demonstrate that in the limited data dimension, the proposed method has slightly higher recommendation accuracy than other representative models and can generate POI sequence easily accessed by current users.