  Sequential recommender systems have demonstrated a huge success for next-item
recommendation by explicitly exploiting the temporal order of users' historical
interactions. In practice, user interactions contain more useful temporal
information beyond order, as shown by some pioneering studies. In this paper,
we systematically investigate various temporal information for sequential
recommendation and identify three types of advantageous temporal patterns
beyond order, including absolute time information, relative item time intervals
and relative recommendation time intervals. We are the first to explore
item-oriented absolute time patterns. While existing models consider only one
or two of these three patterns, we propose a novel holistic temporal pattern
based neural network, named HTP, to fully leverage all these three patterns. In
particular, we introduce novel components to address the subtle correlations
between relative item time intervals and relative recommendation time
intervals, which render a major technical challenge. Extensive experiments on
three real-world benchmark datasets show that our HTP model consistently and
substantially outperforms many state-of-the-art models. Our code is publically
available at https://github.com/623851394/HTP/tree/main/HTP-main
