@7d7n Eugene / others experienced in recommendation systems: for someone who is new to recommendation systems and uses variants of collaborative filtering for recommendations, what non-LLM approach would you suggest to start looking into? The cheaper the compute (ideally without using GPUs in the first place) the better, while also maximizing the performance of the system :)
IMHO it depends on the types of things you are recommending. If you have a good way of accurately and specifically textually classifying items it is hard to beat the performance of good old-fashioned embeddings and vector search/ANN. There are plenty of embeddings that do not need GPU like the newer LLM-based ones all crave. Word2Vec, GloVe, and FastText are all high-performance and you wouldn't need GPUs. There are plenty of vector-search libraries that are high-performance and predate the vector-db popularity of late, so also would not depend on GPUs to be high-performance. Most are memory-hungry however, so something to keep in mind. That performance, especially with the embeddings, will come at the cost of loss of some context. No free lunch.