Pistachio | Low latency , strong consistency | Storage library

You may not get a very good intuitive understanding of usual word2vec behavior using these sorts of product-baskets as training data. The algorithm was originally developed for natural-language texts, where texts are runs of tokens whose frequencies, & co-occurrences, follow certain indicative patterns.

People certainly do use word2vec on runs-of-tokens that aren't natural language - like product baskets, or logs-of-actions, etc – but to the extent such tokens have very-different patterns, it's possible extra preprocessing or tuning will be necessary, or useful results will be harder to get.

As just a few ways customer-purchases might be different from real language, depending on what your "pseudo-texts" actually represent:

• the ordering within a text might be an artifact of how you created the data-dump rather than anything meaningful
• the nearest-neighbors to each token within the window may or may not be significant, compared to more distant tokens
• customer ordering patterns might in general not be as reflective of shades-of-relationships as words-in-natural-language text

So it's not automatic that word2vec will give interesting results here, for recommendatinos.

That's especially the case with small datasets, or tiny dummy datasets. Word2vec requires lots of varied data to pack elements into interesting relative positions in a high-dimensional space. Even small demos usually have a vocabulary (count of unique tokens) of tens-of-thousands, with training texts that provide varied usage examples of every token dozens of times.

Without that, the model never learns anything interesing/generalizable. That's especially the case if trying to create a many-dimensions model (say the default vector_size=100) with a tiny vocabulary (just dozens of unique tokens) with few usage examples per example. And it only gets worse if tokens appear fewer than the default min_count=5 times – when they're ignored entirely. So don't expect anything interesting to come from your dummy data, at all.

If you want to develop an intuition, I'd try some tutorials & other goals with real natural language text 1st, with a variety of datasets & parameters, to get a sense of what has what kind of effects on result usefulness – & only after that try to adapt word2vec to other data.

Negative-sampling is the default, & works well with typical datasets, especially as they grow large (where negative-sampling suffes less of a performance hit than hierarchical-softmax with large vocabularies). But a toggle between those two modes is unlike to cause giant changes in quality unless there are other problems.

Sufficient data, of the right kind, is the key – & then tweaking parameters may nudge end-result usefulness in a better direction, or shift it to be better for certain purposes.

But more specific parameter tips are only possible with clearer goals, once some baseline is working.