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Comment by vector_spaces | Hacker News Reader

A challenge with naive recommender systems in an ecommerce setting is that they tend to recommend the most popular items on your site rather than surfacing the long tail of SKUs, which doesn't generally add much value (you could achieve a similar effect by showing users recommendations from a hardcoded list of top items rather than leaning on a model). If Gorse is generic and unaware of the domain, how does it avoid falling into this trap?

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When I worked for a place with an e-commerce recommender, about the first customer request feature was editorial control on the recommendations, so they could actually sell the things they made money on (or had in stock), rather than what the algorithm thought was best…

Now you've made me curious. If the point is to recommend stuff that people are most likely to buy next then recommending the most popular items is likely correct. So what metric do you have in mind that leads you to the conclusion that recommender engines shouldn't recommend the most popular articles?

Seller satisfaction will be the highest if the user buys the item. Basing the recommender on sales will make it a seller-focused recommender.

If the recommender somehow can be based on buyer satisfaction - it would be a system that focuses on the buyer. It would then perhaps not please the seller.

Taking it one step further, if a buyer is VERY satisfied, the buyer is likely to return for MORE purchases, ensuring the satisfaction of BOTH seller and buyer with the first recommendation. This is the ultimate recommender, measuring not first-purchase buyer/seller satisfaction, but rather measuring "so satisfied with the first purchase that the buyer returned for a second purchase".

To be honest, popular items have more chance to be recommended, and it is hard to avoid. Popular items have higher probability to be liked. In addition, popular items have more data collected, it helps to locate potential users.

Not sure this is true but in any case, the Universal Recommender uses a technique based on the TF-IDF algorithm of search to get long-tail recs. This de-weights popularity so that relevancy is more important.

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