This paper proposes a novel method to estimate appropriate values of products and services by using particle filter and local regression smoothing from many user evaluations in websites such as amazon.com, yelp.com and so on. These websites provide a service which estimates values of the products and services from many user evaluations. However, users cannot always estimate the appropriate value of the product and service. In addition, the product and service cannot always keep a same value. For example, a human being gives a different evaluation to a same dish when he / she is hungry or full. And a mobile phone rises its value by improving connectability as base stations increase. Thus, we need to estimate appropriate values of the products and services by removing noises included in both user evaluations and the value of the products and services. We investigate the effects of the proposed method through simple simulation experiments imitating a reputation information site about restaurants.
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