我是python新手，找不到这个问题的答案。参考消息末尾的代码，我能知道下面一行中的“for item，total in totals.items（）”是什么意思吗？

rankings = [(total/simSums[item], item) for item, total in totals.items()]

另外，代码失败并显示
属性错误：“dict”对象没有属性“predictors”
当我把代码中“item（s）”的所有示例都改为“predictor（s）"时，为什么会这样呢？

# Return the Pearson correlation coefficient for p1 and p2
def sim_person(prefs, p1, p2):
    # Get the list of shared_items
    si={}
    for item in prefs[p1]:
        if item in prefs[p2]:si[item]=1

    # Find the number of elements 
    n=len(si)

    # if they have no ratings in common, return 0
    if n==0: return 0

    # Add up all the preferences
    sum1 = sum([prefs[p1][it] for it in si])
    sum2 = sum([prefs[p2][it] for it in si])

    # Sum up the squares
    sum1Sq = sum([pow(prefs[p1][it],2) for it in si])
    sum2Sq = sum([pow(prefs[p2][it],2) for it in si])

    # Sum up the products
    pSum = sum([prefs[p1][it]*prefs[p2][it] for it in si])

    # Calculate Person score
    num = pSum - (sum1*sum2/n)
    den = sqrt((sum1Sq - pow(sum1,2)/n)*(sum2Sq - pow(sum2,2)/n))
    if den == 0: return 0

    r = num/den
    return r

# Returns the best matches for person from the prefs dictionary.
# Number of results and similarity function are optional params.
def topMatch(prefs, person, n=5, similarity=sim_person):
    scores = [(similarity(prefs, person, other), other) 
              for other in prefs if other!=person]

    # Sort the list so the highest scores appear at the top
    scores.sort()
    scores.reverse()
    return scores[0:n]

# Gets recommendations for a person by using a weighted average
# of every other user's rankings 
def getRecommendations(prefs, person, similarity=sim_person):
    totals = {}
    simSums = {}
    for other in prefs:
        # don't compare me to myself
        if other == person: continue
        sim = similarity(prefs, person, other)

        # ignore scores of zero of lower
        if sim<=0: continue
        for item in prefs[other]:

            # only score movies I haven't seen yet
            if item not in prefs[person] or prefs[person][item]==0:
                # Similarity * Score
                totals.setdefault(item, 0)
                totals[item]+=prefs[other][item]*sim
                # Sum of similarities
                simSums.setdefault(item, 0)
                simSums[item]+=sim

    # Create the normalized list 
    rankings = [(total/simSums[item], item) for item, total in totals.items()]

    # Return the sorted list 
    rankings.sort()
    rankings.reverse()
    return rankings