Number of Reviews * Y + Location * Z In such a scenario, each factor is given a relevance score (how relevant is gender to tasting Mexican food?), then adjusted by machine learning over time to account for personal and broad considerations. range of other factors that would be taken into account. account at the top of this very short list. Let's look at the following illustration (these weight numbers are examples and not indicative of what is actually in the algorithm) : We can get an idea of the weight of each of the factors, with gender hardly affecting them and past ratings of Mexican restaurants taking a heavy weight.
Remember we are reviewing one person here and the value of their feedback on my results. Quite rightly, whether the reviewer is male or female would have very little impact on the weight of their opinion; however, their writing of past reviews of other Mexican jewelry retouching service restaurants, their age being close to mine, and having written a large number of reviews would put more emphasis on their review. If I'm right, in the near future we will see the review system change to give more weight to reviews where the reviewer is
similar to the researcher and generic influence scores will be assigned to individuals (human entities). Also, I would say that it is very likely that not only will the weighting of reviews be adjusted as a result of personalization, but that the search results themselves will be more personalized than they are today. think about products I'm about to slip away to discuss an area that makes sense to me, but I'm just spitting on. We've talked a lot about the impact of reviews on review weighting and a site's relevance to a specific demographic.