Social Relationship Mining Based on User Telephone Communication Data for Cooperative Relationship Recommendation

As one of the main ways of daily communication between friends, telephone communication has a profound impact on people’s life and work. However, this seemingly unimportant information hides a lot of socioeconomic value: the individual’s relationship in the social network. Under normal circumstances, traditional methods only describe these relationships in a coarse-grained manner and classify them, such as relatives, friends, colleagues, classmates, strangers, etc., but cannot reflect and portray the intimacy of the relationship in real life. This paper first proposes a social relationship mining method based on multidimensional attribute association analysis to identify relationships with intense intimacy and then design a cooperative relationship recommendation algorithm based on habit similarity to recommend the best list of cooperative objects. Finally, we use accurate data of 1,549 users’ actual data and protect user privacy through data desensitization. The experimental results show that our method can more truly describe and evaluate the degree of intimacy between friends and quickly identify and recommend the most suitable collaborators.