Diversity and novelty are essential objectives in recommender systems to improve stakeholders’ benefits by reducing user’s discovery efforts and improving business operators’ sales and revenue. Existing diversity and novelty-based methods indifferently increase diversity or novelty for every user, which inevitably induces the trade-off dilemma between relevance and accuracy. Moreover, different users have different preferences for recommendation diversity and novelty. Such preference should be considered by a recommendation algorithm, thereby avoiding the trade-off dilemma and increasing the prediction accuracy. To address this research gap, we propose a new Diversity and Serendipity-Aware Recommender System (DSPA-RS) problem and its solution method. The MovieLens-2k data are used to evaluate our proposed DSPA-RS method against seven widely used recommendation methods in recommender systems as benchmarks. The test results demonstrate our method shows a superior performance than the benchmarks by a range of 34.30% to 108.27%, indicating that the movies recommended by our method best satisfy users’ diversity and serendipity preference. For recommendation accuracy, our DSPARS method outperforms the most accurate method by 34.62% in Precision, 7.71% in Recall, and 24.37% in F1 score. The improvement in recommendation accuracy indicates that DSPA-RS’s consideration and utilization of diversity preference and novelty momentum greatly improves recommendation quality.