This article studies consumer online review behaviors based on recommendation reward programs. It mainly studies the influence of different types of rewards in the recommendation reward plan on consumers’ online review behavior. Through the questionnaire survey, the results show that consumers are more inclined to choose monetary rewards as their return in the recommendation reward program. At the same time, different rewards in the recommendation reward plan, consumer product satisfaction, and the interaction of the two also have a certain impact on consumers’ online review behavior. This article divides the amount of rewards into high rewards and low rewards. When the consumer’s satisfaction reaches a certain level, low rewards in the referral reward program will bring more benefits to the merchants than high rewards. High rewards will only have a greater effect on consumers who are less satisfied with the product. When consumer product satisfaction is not high, and the reward limit set by the recommendation reward plan is low, consumers’ perception of fairness will cause them not to conduct online evaluation behaviors, even if the reward limit is high, the effect is not significant.

Current evaluation methods for soccer player performance either relies on rating from soccer experts or structured statistics of the match, such as shots and tackles. The former needs a lot of manpower and the evaluation is inevitably subjective. The latter can only record the quantity of a player's match events, but cannot reflect the quality (e.g., a wonderful shot or a terrible shot is regarded as a shot). To solve the above problems, an improved evaluation method for soccer player performance using affective computing is proposed. On the basis of statistics, our method also takes advantage of the text information of post-match reports, and employ the affective computing technology to quantify the quality of events. In this way, both the quantity and quality of events are considered. All the players in the Chinese Super League 2019 season are selected as evaluation objects, and the results show that the improved method can evaluate player performance more effectively and reasonably.

Sports big data technology has become an important technological means of modern sports competition. This paper realizes a novel soccer video event detection algorithm based on self-attention. It extracts key frames through self-attention mechanism, and then obtains the characteristics of time window level through NetVLAD network. Finally, each video clip is classified into 4 types of events (goals, red/yellow card, substitutions and others). The experimental results show that with the introduction of the self-attention mechanism, the classification accuracy on the SoccerNet data set has improved from 67.2% to 74.3%.

Automatic protocol mining is a promising approach for inferring accurate and complete API protocols. However, just as with any data-mining technique, this approach requires sufficient training data (object usage scenarios). Existing approaches resolve the problem by analyzing more programs, which may cause significant runtime overhead. In this paper, we propose an inheritance-based oversampling approach for object usage scenarios (OUSs). Our technique is based on the inheritance relationship in object-oriented programs. Given an object-oriented program p, generally, the OUSs that can be collected from a run of p are not more than the objects used during the run. With our technique, a maximum of n times more OUSs can be achieved, where n is the average number of super-classes of all general OUSs. To investigate the effect of our technique, we implement it in our previous prototype tool, ISpecMiner, and use the tool to mine protocols from several real-world programs. Experimental results show that our technique can collect 1.95 times more OUSs than general approaches. Additionally, accurate and complete API protocols are more likely to be achieved. Furthermore, our technique can mine API protocols for classes never even used in programs, which are valuable for validating software architectures, program documentation, and understanding. Although our technique will introduce some runtime overhead, it is trivial and acceptable.