The problem of influence maximization is to select a group of effective seed users in social networks, through which information can reach the largest scope of spread. Traditional researches on influence maximization rely on the specific network structures and diffusion models, however, the manually processed simplified networks and the diffusion models based on assumptions have great limitations on assessing the real influence of users. To solve this problem, an Influence Maximization algorithm based on User Interactive Representation (IMUIR) was proposed. First, the context pairs were constructed through random sampling according to users' interaction traces, and the vector representations of the users were obtained by the SkipGram model training. Then, the greedy strategy was used to select the best seed set according to the activity degrees of the source users and the interaction degrees between these users with other users. To verify the effectiveness of IMUIR, experiments were conducted to compare it with Random, Average Cascade (AC), Kcore and Imfector algorithms on two social networks with real interactive information. The results show that IMUIR selects the seed set with higher quality, produces a wider scope of influence spread, and performs stablely on the two datasets.