In fiercely competitive online retail activities, to improve customer shopping experience and cultivate consumer loyalty, more and more e-commerce websites now begin to provide personalized recommendations for their customers. The earliest studies regarding personalized recommendations focus on the improvement of algorithms or models that improve recommendation quality. However, relatively few studies examine the relationship between personalized recommendations and customer online shopping behavior. It makes scholars and e-commerce practioners place more emphasis on recommendation algorithm itself but neglect the effects of recommendations on consumers and e-commerce websites. To help people better understand personalized recommendations and related research context, this paper comprehensively reviews related literature in three research streams. The first research stream is the definition and classification of personalized recommendations. The second research stream includes investigations and explorations on the recommendation algorithms and statistical models that improve the recommendation quality. The third research stream includes literature on the relationship between personalized recommendations and customer shopping behavior. Different from current literature review, it places emphasis on the relationship between personalized recommendations and consumer shopping behavior, including evaluation of personalized recommendations by consumers and influencing factors and the effects of personalized recommendations on the process and results of consumer online shopping decisions. Finally it provides marketing and information science scholars with some directions in future research.