{'fraction_non_alphanumeric': 0.014705882352941176, 'fraction_numerical': 0.0, 'mean_word_length': 5.5602409638554215, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An approach to the mobile social services recommendation algorithm based on association rules mining

With the continuous development of social network, more and more research areas have emerged. Among them, service recommendation has always been a research hotspot. Yet achieving accurate personalised recommendation is challenging because of the large amount of data generated in social networks as the data available for each user is highly sparse. In this paper, it focuses on the association rules in the mobile social network recommendation algorithms. By introducing the mobile users' location information to the collaborative filtering recommendation process, the association rules between the items are minded. Then the association rules are filtered and split, which are integrated into the similarity matrix to combine user location information with them. Also, an association rule mining model is proposed, which considers time factor to further improved the recommendation accuracy. Experimental results show that the performance of our algorithm is better than the baseline.