Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

The integrated development of manufacturing and modernized service (IDMMS) is an important direction for the transformation and upgrading of manufacturing. Building upon value co-creation, industry chain theory and the industrial boundary literature, this paper explores an analytical framework of IDMMS, combining theoretical and empirical evidence. Then, this study constructs several indicators for measurement of industrial integration. The results indicate that: (1) manufacturing has more self-circulation, and the degree of backward correlation between the modernized services and the manufacturing is weak, while the support and driving effect of the manufacturing is insufficient; (2) most industrial sectors play a relatively stable role in influencing production demand. The findings reveal that there exist several problems referring to industry and enterprises. Finally, this paper discusses the mechanism of IDMMS and reaches its conclusions. The conclusions provide a comprehensive and detailed industrial understanding of the formation of comprehensive and systematic regional economic strategies and suggest targeted industrial policies to achieve sustainable development.