The automatic recognition of user sentiments through their music listening behavior is an important research task in cognitive studies. Whereas prior studies were conducted to identify the sentiment conveyed (or evoked) by a song that a user listens to at a particular time, we argue that a more effective method would be to identify the user’s induced sentiment based on the comprehensive list of songs they have listened to (e.g., the sequence of music being played). However, recognizing the sentiment information induced by a playlist using machine learning techniques is much more challenging than identifying the sentiment induced by a single song, as it is difficult to obtain accurately labeled training samples for playlists. In this study, we developed the List–Song Relationship Factorization (LSRF) model with the objective of efficiently identifying sentiments induced by playlists. This model employs two side information constraints: the sentiment similarity between songs, based on multimodal information, and the co-occurrence of songs in playlists. These constraints enable the simultaneous co-clustering of songs and playlists. The experimental results demonstrate that the proposed model efficiently and consistently identifies sentiment information evoked by either playlists or individual songs.

Recent years have witnessed a rapid increase in online data volume and the growing challenge of information overload for web use and applications. Thus, information diversity is of great importance to both information service providers and users of search services. Based on a diversity evaluation measure (namely, information coverage), a heuristic method—FastCovC+S-Select—with corresponding algorithms is designed on the greedy submodular idea. First, we devise the CovC+S-Select algorithm, which possesses the characteristic of asymptotic optimality, to optimize information coverage using a strategy in the spirit of simulated annealing. To accelerate the efficiency of CovC+S-Select, its fast approximation (i.e., FastCovC+S-Select) is then developed through a heuristic strategy to downsize the solution space with the properties of information coverage. Furthermore, ample experiments have been conducted to show the effectiveness, efficiency, and parameter robustness of the proposed method, along with comparative analyses revealing the performance’s advantages over other related methods. The online appendix is available at https://doi.org/10.1287/ijoc.2017.0753 .

It is a recurrent finding that software development is often troubled by considerable delays as well as budget overruns and several solutions have been proposed in answer to this observation, software fault prediction being a prime example. Drawing upon machine learning techniques, software fault prediction tries to identify upfront software modules that are most likely to contain faults, thereby streamlining testing efforts and improving overall software quality. When deploying fault prediction models in a production environment, both prediction performance and model comprehensibility are typically taken into consideration, although the latter is commonly overlooked in the academic literature. Many classification methods have been suggested to conduct fault prediction; yet associative classification methods remain uninvestigated in this context. This paper proposes an associative classification (AC)-based fault prediction method, building upon the CBA2 algorithm. In an empirical comparison on 12 real-world datasets, the AC-based classifier is shown to achieve a predictive performance competitive to those of models induced by five other tree/rule-based classification techniques. In addition, our findings also highlight the comprehensibility of the AC-based models, while achieving similar prediction performance. Furthermore, the possibilities of cross project prediction are investigated, strengthening earlier findings on the feasibility of such approach when insufficient data on the target project is available.