Over- and underestimation in different product domains

This paper investigates the effects of over and underestimation on the perceived Effectiveness (helpfulness) of recommender systems. We consider four different product along two dimensions, degree of objectivity and investment. Overestimation was considered more severely than underestimation with regard to perceived Effectiveness. Overestimation was also considered more severely in high investment domains compared to low investment domains. In addition, we surveyed the effect of different gaps between initial (initial impression) and final ratings (true estimate). We found that for gaps which remained in the negative half of the scale were considered less Effective than gaps which crossed over from good to bad (or from bad to good), and gaps which remained in the positive half of the scale.