In recent years, the massive influx of information onto internet has facilitated user, not only retrieving information, but also discovering facts. However, web users usually suffer from the information overload problem due to the fact of significantly increasing and rapidly expanding growth in amount of information on the mobile web. Web personalization and recommendation is one of the promising technique to tackle this problem by adapting the content and structure of mobile websites to the requirements of the users by taking benefit of the facts acquired from the analysis of the users’ access behaviors web is another important area which is consists of much more complex structures and huge collection of ambiguous data. The mobile web portion of the web is also quite different from the traditional web. The information contained in the mobile web is often more concise, more location-specific, and time-critical. The multiplicity of mobile web application among assortment of recommendation aim and technique involve that to gather more and more global recommendation requirements, it reasonably significant to implement hybrid recommendations for M- commerce. In this research, we proposed hybrid recommendation systems in m-commerce base on innovative K-means Clustering (IKMC) and Distributed Association Rules together to improve recommendation performance.