Modeling Dynamic Missingness of Implicit Feedback for Recommendation
Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are missing not at random (MNAR); and the missing data is a mixture of negative and unknown feedback, making it difficult to learn users' negative preferences. Recent studies modeled exposure, a latent missingness variable which indicates whether an item is exposed to a user, to give each missing entry a confidence of being negative feedback. However, these studies use static models and ignore the information in temporal dependencies among items, which seems to be an essential underlying factor to subsequent missingness. To model and exploit the dynamics of missingness, we propose a latent variable named "user intent" to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process. The resulting framework captures the dynamic item missingness and incorporate it into matrix factorization (MF) for recommendation. We also explore two types of constraints to achieve a more compact and interpretable representation of user intents. Experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommender systems.
This paper presents H4MF model (HMM + MF for dynamic Missingness) for implicit feedback data. With implicit data, we only observe positive feedback and the missing entries (zeros) in the data can indicate either negative feedback or users are not exposed of the items. H4MF is based on the previous work on modeling user latent exposure (ExpoMF, Liang et al., Modeling user exposure in recommendation, 2016) — the basic idea is that for each user-item pair, there is a latent binary variable to represent exposure; if it’s 1, it means this user is exposed to the item thus 0 feedback mean true negative, while if it’s 0, it means this user have not yet been exposed to this item yet. The difference in H4MF is that H4MF uses a hidden Markov model to capture the temporal dynamics in the user exposure (user intent in this paper). The basic idea is that whether or not a user is exposed to something can be dependent on some other items he/she has been exposed before. The inference is relatively straightforward with some tweaks to overcome the potential redundancy in the learned user latent intents. Empirical results show that H4MF outperforms other similarly-designed baselines.   Overall, the paper is clearly written. However, there are a few aspects that I feel this paper falls a little bit short.   1. The idea of adding temporal dynamics in the latent user intent is fairly natural given the flexible model setup in ExpoMF. I like the motivation example about the purchasing phone and phone case (buying a phone will change the exposure to phone case, but conversely buying a phone case will change the exposure to phone in the opposite direction). However, it is not clear to me how the model can achieve this asymmetric reasoning with H4MF — it might be able to achieve it “by accident” but I cannot see how the model “by design” is able to achieve this. I was expecting a qualitative study on real-world data to convince me this but am quite disappointed to not see it. This is also related to the “causality-based recommendation” aspect mentioned in the paper. In the original ExpoMF paper, a major challenge it faces is how to “explain” exposure — there is an example about some user who listens to a lot of music from a certain band but did not listen to one of their songs — we can come up with two equally plausible explanations to this: either the user is not aware of this song, or clearly he knows this song but does not like it, but without the support of additional data, there is no way we can differentiable these two possibilities and establish the link. I don’t see how H4MF can differ from ExpoMF on this front.   ########## UPDATED AFTER AUTHOR RESPONSE ##########  I want to clarify what I meant by “by accident” as there seems to be some misunderstanding from the author responese. I agree that the temporal HMM model is able to capture the asymmetric relation, my point is more about the model does not have the incentive to capture the motivating example of phone v.s. phone case, which is OK. But in any case, I would be very interested to see a qualitative example demonstrating how H4MF can capture the dynamic missingness in real-world data, similar to the music listening example in ExpoMF.    ###################################################  2. Given a temporal user intent model, it seems only natural to also adopt a temporal user preference model — not necessarily deep, but something along the line of dynamic Poisson factorization (Charlin et al., Dynamic Poisson factorization, 2015) or time SVD++ (Koren, Collaborative Filtering with Temporal Dynamics, 2009) is worth trying. I understand the inference with a static MF model is much simpler, but the reason presented in the paper about the preference being more static is not very convincing.   3. The computational cost is quite high and this significantly limits the usability of H4MF — ExpoMF itself is already not computationally friendly, yet H4MF adds a lot more complexity on top of that. The datasets used in the empirically studies are relatively small sized and yet it still takes hours to train the model. I think stochastic algorithm is a promising avenue to overcome this issue.  ########## UPDATED AFTER AUTHOR RESPONSE ##########  “We implemented ExpoMF in C++ with openMP and it achieved a 30X speedup, compared to the Python implementation.”  Please make it publicly available upon publication of this paper.   ###################################################   Minor comments:  1. Line 8: a essential -> an essential  2. Around Line 40: The phrasing can be improved a little here: is the “missingness probability” referring to non-exposed or true negative? I can roughly infer what the authors mean here from the context, but it would be better to precisely define the term for understanding at first read.  3. Section 4: How is this different from or related to learning the beta prior through empirical Bayes? Updating Beta prior normally mounts to adding the “pseudo-counts” and I don’t think the proposed procedure is clear enough.   4. For implicit feedback data, PMF (Mnih & Salakhutdinov) is not a suitable baseline (as its very subpar results indicate).  5. Maybe time SVD++ or dynamic Poisson factorization is a more suitable baseline than FPMC?  6. Error bars in Table 1?  7. Line 241: Figure 3 -> Figure 2