Providing Consistent Opinions from Online Reviews: A Heuristic Stepwise Optimization Approach
研究了在线评论摘要与排名列表之间的一致性问题，将其转化为优化问题，并提出一种启发式逐步优化方法来近似求解，实验表明该方法优于其他相关方法。
The consistency between review summaries and review ranking lists is important for consumers so they can utilize online reviews effectively and efficiently in their purchase decisions. This paper examines this consistency issue and formulates it as an optimization problem. Based on consumers’ reading behaviors, all possible sets of reviews that consumers would read from ranking lists are considered; the objective is to maximize the expected consistency. Because of the NP-hardness of the problem, exact methods that search for the optimal ranking lists are generally not acceptable in practice. Hence, a heuristic approach (the enhanced stepwise optimization procedure) is proposed. This approach is an effective and efficient approximation that selects reviews iteratively to add to the ranking lists in light of expected consistency value, superiority, and execution time. Intensive experiments on both synthetic and real data are conducted, with various environments and settings, along with a relevant user study, revealing that the proposed approach outperforms other related methods.