This research proposes a new recommender system algorithm for online grocery shopping. The algorithm is based on the perspective that, since the grocery items are usually bought in bulk, a grocery recommender system should be capable of recommending the items in bulk. The algorithm figures out the possible dishes a user may cook based on the items added to the basket and recommends the ingredients accordingly. Our algorithm does not depend on the user ratings. Customers usually do not have the patience to rate the groceries they purchase. Therefore, algorithms that are not dependent on user ratings need to be designed. Instead of using a brute force search, this algorithm limits the search space to a set of only a few probably food categories. Each food category consists of several food subcategories. For example, "fried rice" and "biryani" are food subcategories that belong to the food category "rice". For each food category, items are ranked according to how well they can differentiate a food subcategory. To each food subcategory in the activated search space, this algorithm attaches a score. The score is calculated based on the rank of the items added to the basket. Once the score exceeds a threshold value, its corresponding subcategory gets activated. The algorithm then uses a basket-to-recipe similarity measure to identify the best recipe matches within the activated subcategories only. This reduces the search space to a great extent. We may argue that this algorithm is similar to the content-based recommender system in some sense, but it does not suffer from the limitations like limited content, over-specialization, or the new user problem.

中文翻译：

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基于项目排序方法的杂货推荐算法

这项研究提出了一种用于在线杂货店购物的新推荐系统算法。该算法基于以下观点：既然杂货通常是散装购买的，那么杂货推荐系统应该能够散装推荐商品。该算法根据添加到购物篮中的物品计算出用户可能烹饪的菜肴，并相应地推荐食材。我们的算法不取决于用户评分。客户通常没有耐心来评估他们购买的杂货。因此，需要设计不依赖于用户等级的算法。该算法没有使用蛮力搜索，而是将搜索空间限制为仅几个可能的食物类别的集合。每个食物类别都包含几个食物子类别。例如，“炒饭”和“ 是属于食物类别“大米”的食物子类别。对于每种食物类别，均根据商品对食物子类别的区分程度来对商品进行排名。对于激活的搜索空间中的每个食物子类别，此算法都会附加一个分数。得分是根据添加到购物篮中的物品的等级计算的。一旦分数超过阈值，便会激活其相应的子类别。然后，该算法使用购物篮到食谱的相似性度量来仅在激活的子类别中识别最佳配方匹配。这在很大程度上减少了搜索空间。我们可能会争辩说，该算法在某种意义上类似于基于内容的推荐器系统，但是它不受诸如内容有限，过度专业化或新用户问题之类的局限性的困扰。是属于食物类别“大米”的食物子类别。对于每种食物类别，均根据商品对食物子类别的区分程度来对商品进行排名。对于激活的搜索空间中的每个食物子类别，此算法都会附加一个分数。得分是根据添加到购物篮中的物品的等级计算的。一旦分数超过阈值，便会激活其相应的子类别。然后，该算法使用购物篮到食谱的相似性度量来仅在激活的子类别中识别最佳配方匹配。这在很大程度上减少了搜索空间。我们可能会争辩说，该算法在某种意义上类似于基于内容的推荐器系统，但是它不受诸如内容有限，过度专业化或新用户问题之类的局限性的困扰。